Feature Selection Support Vector Machine Research Articles

Colloid Transport Prediction using SVM-MLP

The effectiveness of the multilayer perceptron (MLP) method in conjunction with support vector machines (SVM) for the prediction of colloid transport behaviour is examined in this work. Because colloids are essential to many industrial and environmental processes, proper modelling is required for efficient management. By using SVM for feature selection and MLP for nonlinear mapping, the suggested SVM-MLP hybrid technique combines the advantages of both algorithms to improve prediction accuracy. After a great deal of testing and verification, the model shows encouraging outcomes that highlight its ability to forecast colloid transport dynamics more accurately and efficiently, providing important information for industrial and environmental applications.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

Predicting the risk of in-hospital Asian NSTEMI patients using stacked ensemble learning

Abstract Background NSTEMI affects 60–75% of patients with MI and has a variety of clinical manifestations and prognoses. Ensemble learning (EL) utilizes the machine learning (ML) models on a classification task to make more accurate predictions than any single ML model. It has also been demonstrated to be superior to conventional risk scores in predicting mortality risk. However, no studies on the application of EL to ASIAN NSTEMI patients have been published. Purpose To identify factors associated with Asian in-hospital NSTEMI patients using ML and to develop a stacking EL-based NSTEMI risk score that is specifically tailored to the Asian population. Methods From 2006 to 2016, 10464 data were collected from the Malaysian National Cardiovascular Disease Database (NCVD-ACS) registry on hospitalized multiethnic Asian patients admitted with NSTEMI. This study utilized 39 variables, including demographic, cardiovascular risk, medication, and clinical variables. For stacked EL model development, four algorithms, Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Naive Bayes (NB), are used as base learners, and Generalized Linear Model (GLM) is used as the meta learner. Significant variables were chosen and ranked using ML variable importance with backward elimination. As a predictive performance metric, the area under the curve (AUC) was used. Using a validation dataset, algorithms were validated against the TIMI for NSTEMI, and the net reclassification index (NRI) was calculated. Results The stacked EL models developed using SVM feature selection (13 predictors, AUC = 0.823, CI: 0.779 - 0.867) outperforms the conventional risk score, TIMI (AUC = 0.563, CI: 0.515 - 0.610). RF contributes the most to the EL model predictions, followed by NB, XGB and SVM (Figure 1). Common predictors between SVM feature selection and TIMI are; age, hypertension and ECG-type depression. SVM feature selection also identified gender, MI history, chronic lung disease, heart rate, systolic blood pressure, Killip class, fasting blood glucose, LMWH, beta-blockers and angiotensin II receptor blocker as predictors that improve mortality prediction in ASIAN NSTEMI patients. Our findings indicate that the TIMI score underestimates the mortality risk of NSTEMI patients. Stacked EL model using selected predictors classified 47.4% of nonsurvivors as high risk (risk probabilities > 50%), whereas TIMI (score > 5) classified only 3.3% of nonsurvivors as high risk (Figure 2). The NRI of NSTEMI patients using the EL model (SVM selected var) resulted in an NRI of 79.8% with a p-value < 0.00001 when compared to the TIMI for NSTEMI risk score. Conclusions The stacked EL algorithm classifies ASIAN NSTEMI patients more accurately than the single ML model and TIMI score. A precise prognosis is useful for selecting the appropriate level of care and pharmacological or invasive treatment for patients with Asian NSTEMI.Relative influence of each ML modelsStacked EL performance on validation set

An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid

Accurate load forecasting plays a crucial role in the effective energy management of smart cities. However, the smart cities’ residents’ load profile is nonlinear, having high volatility, uncertainty, and randomness. Forecasting such nonlinear profiles requires accurate and stable prediction models. On this note, a prediction model has been developed by combining feature preprocessing, a multilayer perceptron, and a genetic wind-driven optimization algorithm, namely FPP-MLP-GWDO. The developed hybrid model has three parts: (i) feature preprocessing (FPP), (ii) a multilayer perceptron (MLP), and (iii) a genetic wind-driven optimization (GWDO) algorithm. The MLP is the key part of the developed model, which uses a multivariate autoregressive algorithm and rectified linear unit (ReLU) for network training. The developed hybrid model known as FPP-MLP-GWDO is evaluated using Dayton Ohio grid load data regarding aspects of accuracy (the mean absolute percentage error (MAPE), Theil’s inequality coefficient (TIC), and the correlation coefficient (CC)) and convergence speed (computational time (CT) and convergence rate (CR)). The findings endorsed the validity and applicability of the developed model compared to other literature models such as the feature selection–support vector machine–modified enhanced differential evolution (FS-SVM-mEDE) model, the feature selection–artificial neural network (FS-ANN) model, the support vector machine–differential evolution algorithm (SVM-DEA) model, and the autoregressive (AR) model regarding aspects of accuracy and convergence speed. The findings confirm that the developed FPP-MLP-GWDO model achieved an accuracy of 98.9%, thus surpassing benchmark models such as the FS-ANN (96.5%), FS-SVM-mEDE (97.9%), SVM-DEA (97.5%), and AR (95.7%). Furthermore, the FPP-MLP-GWDO significantly reduced the CT (299s) compared to the FS-SVM-mEDE (350s), SVM-DEA (240s), FS-ANN (159s), and AR (132s) models.

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study.

To develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric 18F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome. The 18F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models' interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis. Among the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863. The integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric 18F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

Cost-sensitive max-margin feature selection for SVM using alternated sorting method genetic algorithm

This article introduces Alternated Sorting Method Genetic Algorithm (ASMGA), a simultaneous feature selection and model selection algorithm for Support Vector Machine (SVM) classifiers. It is a hybrid wrapper-filter algorithm that combines Genetic Algorithm (GA) with Max-Margin Feature Selection (MMFS). MMFS is a filter model that estimates feature importance based on feature relevance and redundancy. In this research, the idea of different error costs was used to introduce cost sensitivity to ASMGA. Thus, ASMGA selects relevant and independent features in a cost-sensitive manner. This research investigates the relationship between the cost sensitivity of SVM models produced by ASMGA and the feature selection process. ASMGA approximates a set of Pareto optimal feature subsets based on three objectives: cost-sensitive error rate, feature subset size, and MMFS-based estimates of feature relevance and redundancy. This research introduces a technique for handling multiple objectives. During the search, ASMGA alternates between two multi-objective sorting techniques: Weighted Sum (WS) of objectives and Non-dominated Sorting (NDS), according to a schedule of methods. This technique allows ASMGA to work as elitist GA for some iterations and as a Non-dominated Sorting Genetic Algorithm (NSGA-II) for the remaining iterations. The proposed algorithm was tested on 11 benchmark datasets and compared to canonical GA and NSGA-II. The algorithm and its variations performed on average 3.8% better than GA and NSGA-II for balanced datasets and 6.6% better for imbalanced datasets. The results and analysis in this article showcase the potential of ASMGA and help explain the interaction between cost sensitivity and feature selection.

Linear Cost-sensitive Max-margin Embedded Feature Selection for SVM

The information needed for a certain machine application can be often obtained from a subset of the available features. Strongly relevant features should be retained to achieve desirable model performance. This research focuses on selecting relevant independent features for Support Vector Machine (SVM) classifiers in a cost-sensitive manner. A review of recent literature about feature selection for SVM revealed a lack of linear programming embedded SVM feature selection models. Most reviewed models were mixed-integer linear or nonlinear. Further, the review highlighted a lack of cost-sensitive SVM feature selection models. Cost sensitivity improves the generalization of SVM feature selection models, making them applicable to various cost-of-error situations. It also helps with handling imbalanced data. This research introduces an SVM-based filter method named Knapsack Max-Margin Feature Selection (KS-MMFS), which is a proposed linearization of the quadratic Max-Margin Feature Selection (MMFS) model. MMFS provides explicit estimates of feature importance in terms of relevance and redundancy. KS-MMFS was then used to develop a linear cost-sensitive SVM embedded feature selection model. The proposed model was tested on a group of 11 benchmark datasets and compared to relevant models from the literature. The results and analysis showed that different cost sensitivity (i.e., sensitivity–specificity tradeoff) requirements influence the features selected. The analysis demonstrated the competitive performance of the proposed model compared with relevant models. The model achieved an average improvement of 31.8% on classification performance with a 22.4% average reduction in solution time. The results and analysis in this research demonstrated the competitive performance of the proposed model as an efficient cost-sensitive embedded feature selection method.

Investigating performance of deep learning and machine learning risk stratification of Asian in-hospital patients after ST-elevation myocardial infarction

Abstract Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Investigating performance of deep learning and machine learning risk stratification of Asian in-hospital patients after ST-elevation myocardial infarction

Abstract Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

ACS mortality prediction in Asian in-hospital patients with deep learning using machine learning feature selection

Abstract Background Thrombolysis in Myocardial infarction (TIMI) is used in predicting the mortality rate of the acute coronary syndrome (ACS) patients. TIMI was developed based on the Western cohort with limited data on the Asian cohort. There are separate TIMI scores for STEMI and NSTEMI. Deep learning (DL) and machine learning (ML) algorithms such as support vector machine (SVM) in population-specific dataset resulted in a higher area under the curve (AUC) to TIMI. The limitation of DL is selected features by the algorithm is unknown compared to ML algorithms. Purpose To construct a single in-hospital mortality risk scoring system that combines SVM feature importance and the DL algorithm in ASIAN patients with ACS that is applicable for both STEMI and NSTEMI patients. To investigate DL performance constructed using predictors selected from SVM feature extraction and DL using complete features and compare with TIMI risk score for STEMI and NSTEMI patients. Methods We constructed four algorithms: i) DL and SVM algorithm with feature selected from SVM variable importance, ii) DL and SVM algorithm without feature selection. SVM feature importance with the backward elimination method is used to select and rank important variables. We used registry data from the National Cardiovascular Disease Database of 13190 patient's data. Fifty-four parameters including demographics, cardiovascular risk, medications and clinical variables were considered. AUC was used as the performance evaluation metric. All algorithms were validated using validation dataset and compared to the conventional TIMI for STEMI and NSTEMI. Results Validation results in Figure 1 are by STEMI and NTEMI patients. Both DL algorithms outperformed ML and TIMI score on validation data. Similar performance is observed for DL and SVM algorithms using all predictors (54 predictors) with DL and SVM algorithm using selected predictors (14 predictors). Predictors selected by the SVM feature selection are: age, heart rate, Killip class, fasting blood glucose, ST-elevation, CABG, cardiac catheterization, angina episode, HDLC, LDC, other lipid-lowering agents, statin, anti-arrhythmic agent, oralhypogly. CABG and pharmacotherapy drugs as selected predictors improve mortality prediction compared to TIMI score. In DL, 25.87% of STEMI patients and 19.71% of NSTEMI patients are estimated as high risk (risk probabilities of >50%). TIMI underestimated the risk of mortality of high-risk patients (≥5 risk scores) with 13.08% from STEMI patients and 4.65% from NSTEMI patients (Figure 2). Conclusions In the ASIAN multi-ethnicity population, patients with ACS can be better classified using one single algorithm compared to the conventional method like TIMI which requires two different scores. Combining ML feature selection with DL allows the identification of distinct factors related to in-hospital mortality of ACS patients in a unique ASIAN population for better mortality prediction. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 Figure 1. Performance resultsFigure 2. Analysis on the validation set

Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM

Simultaneous feature selection and classification have been explored in the literature to extend the support vector machine (SVM) techniques by adding penalty terms to the loss function directly. However, it is the kernel function that controls the performance of the SVM, and an imbalance in the data will deteriorate the performance of an SVM. In this paper, we examine a new method of simultaneous feature selection and binary classification. Instead of incorporating the standard loss function of the SVM, a penalty is added to the data-adaptive kernel function directly to control the performance of the SVM, by firstly conformally transforming the kernel functions of the SVM, and then re-conducting an SVM classifier based on the sparse features selected. Both convex and non-convex penalties, such as least absolute shrinkage and selection (LASSO), moothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) are explored, and the oracle property of the estimator is established accordingly. An iterative optimization procedure is applied as there is no analytic form of the estimated coefficients available. Numerical comparisons show that the proposed method outperforms the competitors considered when data are imbalanced, and it performs similarly to the competitors when data are balanced. The method can be easily applied in medical images from different platforms.

Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy

Electronic Commerce (E-Commerce) is distributing, buying, selling, and marketing goods and services over electronic systems such as the Internet, television, websites, and other computer networks. E-commerce platforms such as amazon.com and Lazada.co.id offer products with various price and quality. Sentiment analysis used to understand the product’s popularity based on customers’ reviews. There are some approaches in sentiment analysis including machine learning. The part of machine learning that focuses on text processing called text mining. One of the techniques in text mining is classification and Support Vector Machine (SVM) is one of the frequently used algorithms to perform classification. Feature and parameter selection in SVM significantly affecting the classification accuracy. In this study, we chose unigram as the feature extraction and grid search as parameter optimization to improve SVM classification accuracy. Two customer review datasets with different language are used which is Amazon reviews that written in English and Lazada reviews in the Indonesian language. 10-folds cross validation and confusion matrix are used to evaluating the experiment results. The experiment results show that applying unigram and grid search on SVM algorithm can improve Amazon review accuracy by 26,4% and Lazada reviews by 4,26%.

A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm

Basal stem rot (BSR) disease in oil palm is caused by Ganoderma boninense fungus. This plant disease is deemed highly destructive and would cause substantial economic loss. The use of spectroscopy technique with the capacity to deal with a large amount of spectral data has gained growing attention as a robust method, particularly to identify the symptoms of plant disease in its initial stage. The dimensionality reduction is pivotal in the use of spectroscopy technique due to its improved prediction performance and optimum processing. Considering that, this study assessed the feasibility of utilising dielectric spectral properties to classify the severity levels of BSR disease in oil palm across a frequency range of 100 kHz–30 MHz. The support vector machine-feature selection (SVM-FS) and principal component analysis (PCA) were applied as data reduction methods. After selecting the optimum number of significant frequencies, this study proceeded to assess the effectiveness of the support vector machine (SVM) and quadratic discriminant analysis (QDA) classifiers in identifying the four different levels of BSR disease. The performance of both classifiers with and without data reduction methods was subsequently compared in terms of the classification accuracy, while the whole spectrum data served as part of the control method. The resultant outcomes revealed that the use of QDA classifier with PCA recorded the highest classification accuracy (up to 96.36%). As for the case of without using data reduction methods, the SVM classifier recorded the highest classification accuracy at only 79.55%. Conclusively, this study proved the significance of dimensionality reduction of dielectric spectral data for the classification of BSR disease in oil palm.

Feature selection in SVM via polyhedral k-norm

We treat the Feature Selection problem in the Support Vector Machine (SVM) framework by adopting an optimization model based on use of the L-0 pseudo-norm. The objective is to control the number of non-zero components of normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral k-norm , intermediate between L-1 and L-∞ norms, plays a significant role, allowing us to come out with a DC (Difference of Convex) optimization problem that is tackled by means of DCA algorithm. The results of several numerical experiments on benchmark classification datasets are reported.

Impact of feature selection on system identification by means of NARX-SVM

Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time.

Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer\u2019s Disease

Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer’s disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

Today, there is a far reaching of Internet benefits everywhere throughout the world, numerous sorts and vast number of security dangers are expanding. Since it isn't in fact possible to assemble a framework without any vulnerability, Intrusion Detection System (IDS), which can successfully distinguish Intrusion, gets to have pulled in consideration. Intrusion detection can be characterized as the way toward distinguishing irregular, unauthorized or unapproved action that objective is to target a system and its assets. IDS plays a very important role for analyzing the network passage, also it assumes a key part to analyze the system activity log and each log is portrayed by huge arrangement of highlights and it requires tremendous computational preparing force and time for the characterization procedure. This work proposes filter based feature selection methods to predict intrusion with Feature based Mutual Information Feature Selection Support Vector Machine (FMIFSSVM), Feature based Liner Correlation Feature Selection Support Vector Machine (FLCFSSVM), misuses SVM, anomaly SVM and Bayesian methods. The performances of these methods are considered by using the intrusion detection calculation data set called Knowledge Discovery in Databases (KDD) cup 99. Detection Rate (DR), False Alarm Rate (FAR) and Percentage of Successful Prediction (PSP) are the major performance measures studied in this work.

Radiomics Analysis: Complemental But Unessential for Routine Clinical Practice in Predicting Microvascular Invasion of Hepatocellular Carcinoma

Background: Microvascular invasion (MVI) is a prognostic factor of patients with hepatocellular carcinoma (HCC). Preoperative prediction of MVI is challenging. Use of Radiomics as clinical biomarker necessitates amelioration. We therefore estimated whether Radiomics can provide complementary value for predicting MVI risk in HCC patients as compared with a liver imaging score (LI-score) system derived from radiologists' interpretation. Methods: 495 patients (300 in training, 50 in validation and 145 in testing data set) with histologically confirmed HCC between Jan 2009 and Aug 2017 were retrospectively reviewed. All the patients have intact preoperative clinicoradiological records. 93 radiomic features were extracted from each patient and radiomics signature were built by using recursive feature selection support vector machine (RFE-SVM) method. Odds-ratio (OR) regression model was performed to determine risk factors of MVI and construct the predictive nomogram. A decision curve analysis was applied to teste the incremental value of Radiomics score (RS) to LI-score for predicting MVI. Findings: 9 radiomics features were selected to build a radiomics signature that was significantly associated with MVI (p < 0.001). An MVI risk estimation nomogram, which incorporated higher aspartate aminotransferase (AST), higher α-fetoprotein (AFP), non-smooth tumor margin, extrahepatic growth pattern, ill-defined pseudo-capsule, peritumoral arterial enhancement, positive radio-genomic venous invasion (RVI) score and higher RS, was established with an area under curve (AUC) of 0.894 (95%CI, 0.864-0.920). Adding RS to LI-score scheme did not achieve significant improvement in predictive performance and did not add additional net benefits in a decision curve analysis. Interpretation: Radiomics is still a complemental tool that offer limited additional benefit to conventional radiologic method for predicting MVI risk of HCC. The use of Radiomics as clinical biomarkers still needs further study. Funding: China Postdoctoral Fund. A Key Social Development Program for the Ministry of Science and Technology of Jiangsu Province. Declaration of Interest: The authors have declared no conflicts of interest. Ethical Approval: This retrospective study involved routine at a single medical institution. Ethics committee approval was granted by local institutional ethics review board with a waiver of written informed consent. All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments.

Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy

Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz–30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p < .1). The impedance values were highly classified by Ganoderma disease at different levels of severity with overall accuracies of more than 80%. Impedance can be considered as the best electrical properties that can be used to estimate the severity of BSR disease in oil palm using spectroscopy technique. As such, this study demonstrates the potentials of utilizing electrical properties for detection of Ganoderma diseases in oil palm.

Lagrangian relaxation for SVM feature selection

We discuss a Lagrangian-relaxation-based heuristics for dealing with feature selection in the Support Vector Machine (SVM) framework for binary classification. In particular we embed into our objective function a weighted combination of the L1 and L0 norm of the normal to the separating hyperplane. We come out with a Mixed Binary Linear Programming problem which is suitable for a Lagrangian relaxation approach.Based on a property of the optimal multiplier setting, we apply a consolidated nonsmooth optimization ascent algorithm to solve the resulting Lagrangian dual. In the proposed approach we get, at every ascent step, both a lower bound on the optimal solution as well as a feasible solution at low computational cost.We present the results of our numerical experiments on some benchmark datasets.