Genetic Algorithm For Feature Selection Research Articles

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.

Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.

This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as “Extra Tree Classifier” and “Logistic Regression” to predict the churn customers.

Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms <i>Chi</i>² and <i>PCA</i> feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.

Development of an intelligent clinical decision support system for the early prediction of diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus (DM) and is identified as a leading cause of the end-stage renal disease (ESRD). Considering the importance of early prediction of individuals at risk of this complication, the use of intelligent models through machine learning (ML) algorithms can be helpful. Therefore, this study aimed to identify the influential variables in predicting DN and fed them as inputs to develop an ML-based decision support system (DSS) for DN diagnosis. MethodsThe data of 327 patients with diabetes (types 1 and 2) were retrospectively analyzed. After data preparation, the genetic algorithm (GA) feature selection method was used to identify the predictor variables affecting DN. Then, several ML algorithms, including the support vector machine (SVM), decision tree (DT), K-nearest neighbors (KNN), and artificial neural networks (ANN) were used to train predictive models based on the selected features. Afterward, the performance of the developed models was evaluated using sensitivity, specificity, and accuracy criteria in 10 independent runs. Finally, the DSS was developed based on the best fit model in the C# programming language. ResultsOur findings illustrated that age, hemoglobin A1c (HbA1c) test, diastolic arterial pressure (DAP), systolic arterial pressure (SAP), fasting glycemia rate (FGR), and DM involvement time were the most important factors in predicting DN. Moreover, to predict the DN, GA combined with the DT algorithm obtained the highest performance in terms of accuracy, sensitivity, specificity, and area under the curve (AUC), equal to 98.9, 98.6, 99.2, and 98.9%, respectively. ConclusionsThe results revealed that GA combined with the DT classifier predicted DN with significant accuracy. Thus, the DSS developed based on DT can be considered a reliable tool to help physicians make decisions. Future studies are warranted to further validate the applicability of our model in clinical settings.

A Genetic Algorithm and PCA-Based Feature Selection to Improve the Failure Diagnosis Performance of Railway Vehicle Doors

The failure diagnosis of railway vehicle door system is carried out using a test bench and machine learning software for the fast and accurate classification. The signal length deviation exists in actual collected data of normal operation and abnormal failures with a time delay. The traditional data multi-segmentation technique for feature extraction has shortcomings by assuming that the measured time-based signals have the same operating time. However, the uniform data segmentation has a difficulty due to the deviation of measured data length. A method of converting time-based data into position-based data was performed to overcome the deviation problem. A method of optimized single-zone data using a genetic algorithm was proposed to improve the classification performance and to reduce computation time, instead of existing the multi-segmentation technique. A principal component analysis-based feature dimensional reduction with explained variance ratio was used to reduce the effect from multi-collinearity of features. Finally, the combination of the proposed methods was compared with individual methods to validate the classification performance by using support vector machine and other classifiers. It was confirmed that the proposed combination method shows the highest classification accuracy of 99.84%.

Multi-Timescale Wavelet Scattering With Genetic Algorithm Feature Selection for Acoustic Scene Classification

In this paper, we apply a genetic algorithm (GA) for feature selection, wrapper approach, on wavelet scattering (WS) second-order coefficients to reduce the large frequency dimension (>500). The evaluation demonstrates the capability of GA to reduce the dimension space by approximately 30% while ensuring a minimum performance drop. Furthermore, the reduced WS directly impacts the training time of the convolutional neural network, by reducing the computational time by 20% to 32%. The paper extends its scopes to explore GA for feature selection on multiple timescales of WS: 46ms, 92ms, 185ms, and 371ms. Incorporating multiple timescales has improved classification performance (by ~ around 2.5%) as an acoustic representation usually contains information at different time scales. However, it can increase computational cost due to the larger frequency dimension of 1851. With the application of GA for feature selection, the frequency dimension is reduced by 50%, saving around 40% computational time, thus increasing the classification performance by 3% compared to a vanilla WS. Lastly, the entire implementations are evaluated using the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 dataset, and the proposed multiple timescales model achieves 73.32% of classification accuracy.

SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition.

Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.

USE OF GAUSSIAN PROCESS TO MODEL, PREDICT AND EXPLAIN HUMAN EMOTIONAL RESPONSE TO CHINESE TRADITIONAL MUSIC

Music listening is one of the most enigmatic of human mental phenomena; it not only triggers emotions but also changes our behavior. During the music session many people are observed to exhibit varying emotional response, which can be influenced by diverse factors such as music genre and instrument as well as the personal attributes of audiences. In this study, we assume that there is an intrinsic, complex and implicit relationship between the basic sound features of music and human emotional response to the music. The response levels of 12 individuals to a representative repertoire of 36 classical/popular Chinese traditional music (CTM) are systematically analyzed using the chills as a quantitative indicator, totally resulting in 432 ([Formula: see text]) CTM–individual pairs that define a systematic individual-to-music response profile (SPTMRP). Gaussian process (GP) is then employed to model the multivariate correlation of SPTMRP profile with 15 sound features (including 5 Timbres, 4 Rhythms and 6 Pitchs) and 5 individual features in a supervised manner, which is also improved by genetic algorithm (GA) feature selection and compared with other machine learning methods. It is shown that the built GP regression model possesses a strong internal fitting ability ([Formula: see text]) and a good external predictive power ([Formula: see text]), which performed much better than linear PLS and nonlinear SVM and RF, confirming that the human emotional response to music can be quantitatively explained by GP methodology. Statistical examination of the GP model reveals that the sound features contribute more significantly to emotional response than individual features; their importance increases in the order: [Formula: see text], in which the spectral centroid (SC), relative amplitude of salient peaks (RASP), ratio of peak amplitudes (RPA), sum of all rhythm histograms (SARH) and period of unfolded maximum peak (PUMP) as well as gender are primarily responsible for the response.

On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model.

Accelerating Analytics Using Improved Binary Particle Swarm Optimization for Discrete Feature Selection

Abstract Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved Binary Particle Swarm Optimization (IBPSO) to optimally identify subset of features. The problem of stagnation, trapping in local optima and premature convergence of Binary Particle Swarm Optimization (BPSO) for solving discrete feature selection dispute has been tackled using IBPSO. IBPSO prevents the model from overfitting and also takes less computational time for constructing the model because of reduced feature subset. The sine function, cosine function, position of the random particle and linear decrement of inertial weight are integrated in IBPSO, which balances between exploration and exploitation to identify optimal subset of features. The linear decrement of inertial weight tends to do good level of exploration at the starting phase, whereas at the end it tends to exploit solution space to find the optimal subset of features that are more informative and thereby discarding redundant and irrelevant features. Experimentation is carried out on seven benchmarking datasets obtained from University of California, Irvine repository, which includes various real-world datasets for processing with machine learning algorithms. The proposed IBPSO is compared with conventional metaheuristic algorithms such as BPSO, Simulated Annealing, Ant Colony Optimization, Genetic Algorithm and other hybrid metaheuristic feature selection algorithms. The result proves that IBPSO maximizes the accuracy of the classifier together with maximum dimensionality reduction ratio. Also, statistical tests such as T-test, Wilcoxon signed-pair test are also carried out to demonstrate IBPSO is better than other algorithms taken for experimentation with confidence level of 0.05.

A quantitative structure–activity relationship study on CXL017 derivatives as effective drugs for cancer treatment

AbstractA novel quantitative structure–activity relationship study was established on a set of ethyl‐2‐amino‐6‐(3.5‐dimethoxyphenyl)‐4‐(2‐ethoxy‐2‐oxoethyl)‐4H‐chromene‐3 carboxylate (CXL017) derivatives to predict their half‐maximal inhibitory concentration (IC50) values. A principal component analysis pattern was first used to divide the whole data set into training and test sets. Then, stepwise (SW) and genetic algorithm (GA) feature selection approaches were selected to choose the proper molecular descriptors which were then subjected to multiple linear regression (MLR). Accordingly, linear models consisting of seven and six molecular descriptors were constructed and their stability was further checked using leave‐one‐out (LOO) and leave group out (LGO) cross‐validation techniques. In addition, the 15 compounds of the test set were further directed to external statistical validation to assess the forecasting capability of the generated models. A simple comparison of the results obtained by the two methodologies showed that the GA‐MLR model had some superiorities versus the SW‐MLR modeling approach, for example, R2train = 0.832, R2test = 0.816, Q2LOO = 0.788 and Q2LGO = 0.796, Ftrain = 44.01, Ftest = 6.67, MSEtrain = 0.069, MSEtest = 0.053 for the computation of the relevant IC50 values. Based on the information derived from the generated models, some key features are identifiable to appraise the activity of the organic drugs that can be used to design new anticancer agents.

Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification

Feature selection is a key pre-processing technique for classification which aims at removing irrelevant or redundant features from a given dataset. Generally speaking, feature selection can be considered as a multi-objective optimization problem, i.e, removing number of features and improving the classification accuracy. Genetic algorithms (GAs) have been widely used for feature selection problems. The crossover operator, as an important technique to search for new solutions in GAs, has a strong impact on the final optimization results. However, many crossover operators are problem-dependent and have different search abilities. Thus, it is a challenge to select the most efficient one to solve different feature selection problems, especially when the nature of feature selection problems is unknown in advance. In order to overcome this challenge, in this paper, a multi-objective binary genetic algorithm integrating an adaptive operator selection mechanism (MOBGA-AOS) is proposed. In MOBGA-AOS, five crossover operators with different search characteristics are used. Each of them is assigned a probability based on the performance in the evolution process. In different phases of evolution, the proper crossover operator is selected by roulette wheel selection according to the probabilities to produce new solutions for the next generation. The proposed algorithm is compared with five well-known evolutionary multi-objective algorithms on ten datasets. The experimental results reveal that MOBGA-AOS is capable of removing a large amount of features while ensuring a small classification error. Moreover, it obtains prominent advantages on large-scale datasets, which demonstrates that MOBGA-AOS is competent to solve high-dimensional feature selection problems.

Identification of the miRNA signature associated with survival in patients with ovarian cancer.

Ovarian cancer is a major gynaecological malignant tumor associated with a high mortality rate. Identifying survival-related variants may improve treatment and survival in patients with ovarian cancer. In this work, we proposed a support vector regression (SVR)-based method called OV-SURV, which is incorporated with an inheritable bi-objective combinatorial genetic algorithm for feature selection to identify a miRNA signature associated with survival in patients with ovarian cancer. There were 209 patients with miRNA expression profiles and survival information of ovarian cancer retrieved from The Cancer Genome Atlas database. OV-SURV achieved a mean correlation coefficient of 0.77±0.01and a mean absolute error of 0.69±0.02 years using 10-fold cross-validation. Analysis of the top ranked miRNAs revealed that the miRNAs, hsa-let-7f, hsa-miR-1237, hsa-miR-98, hsa-miR-933, and hsa-miR-889, were significantly associated with the survival in patients with ovarian cancer. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that four of these miRNAs, hsa-miR-182, hsa-miR-34a, hsa-miR-342, and hsa-miR-1304, were highly enriched in fatty acid biosynthesis, and the five miRNAs, hsa-let-7f, hsa-miR-34a, hsa-miR-342, hsa-miR-1304, and hsa-miR-24, were highly enriched in fatty acid metabolism. The prediction model with the identified miRNA signature consisting of prognostic biomarkers can benefit therapeutic decision making of ovarian cancer.

Optimization of the C4.5 Algorithm by Using a Genetic Algorithm for the Diagnosis of Life Expectancy for Hepatitis Patients

As technology develops rapidly, the amount of data generated experiencing rapid development, including medical data. Data can help diagnose the life expectancy of people with the disease such as hepatitis using data mining methods in the medical field. In this research, technique data mining uses a classification technique with the C4.5 algorithm and the UCI Machine Learning Repository dataset. This dataset has 19 attributes, 1 class, and 155 samples. C4.5 algorithm is optimized using the Genetic Algorithm feature selection process. This study compares the accuracy of the C4.5 algorithm before and after optimization using a Genetic Algorithm. C4.5 algorithm produces the highest accuracy of 96.23%. Meanwhile, the C4.5 algorithm, after being optimized using Genetic Algorithm, has the highest accuracy of 98.11%. The number of features selected is 15 features. Application of Genetic Algorithms in C4.5 algorithm is proven to improve the accuracy in diagnosing life expectancy of people with hepatitis as much as 1.88%.

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.

A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection

With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications. However, there are different threats could make the corporations vulnerable for potential attacks. One of these threats is harnessing the domain name protocol for passing harmful information, this kind of threats is known as DNS tunneling. As a result, confidential information would be exposed and violated. Several studies have investigated the machine learning in order to propose a detection approach. In their approaches, authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper proposes a hybrid method of genetic algorithm feature selection approach with the support vector machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure

Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification

It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.

Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm

Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm