Optimal Feature Subset Research Articles

Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model

Submarine pipelines play an important role in the oil and gas transportation. However, pipeline corrosion can cause the structural degradation of pipelines, which may lead to failure accident and environmental pollution. Pipeline degradation prediction based on monitoring data is of great significance for preventing corrosion failure. In this paper, an ensemble learning (EL) based approach integrated with multi-objective optimization is developed for pipeline corrosion degradation prediction. Firstly, the engineering theory and domain knowledge are integrated into feature engineering to improve the interpretability. Several new feature variables are constructed based on the corrosion mechanism and empirical models. Secondly, an improved multi-objective seagull optimization algorithm (IMOSOA) is proposed to optimize the hyper-parameters of EL model. Subsequently, a novel data-driven model, so-called theory-guided IMOSOA-EL is proposed for the corrosion rate prediction. And six benchmark functions are used to verify the optimization performance of IMOSOA. Then, different feature subsets are developed based on correlation analysis. A comprehensive evaluation indicator is proposed for the optimal feature subset selection. The results demonstrate that the proposed model presents superiority in prediction accuracy compared with other models. This study is significant for reliability assessment and maintenance decision-making of submarine pipelines.

Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning algorithms may be caused by unimportant features that increase the time and memory required to build a classifier. Feature selection (FS) is one of the efficient approaches to reducing the unimportant features. This paper, therefore, presents a new FS, named BDE-BSS-DR, that utilizes Binary Differential Evolution (BDE), Binary Stochastic Search (BSS) algorithm, and Dimensionality Reduction (DR) mechanism. The BSS algorithm increases the search capability of the BDE by escaping from local optimal points and exploring the search space. The DR mechanism then reduces the dimensions of the search space gradually. As a result of using DR, the local optima of the search space and the problem of wrong removal of important features before starting the search process are reduced. The algorithm's efficiency is evaluated on 20 different medical datasets. The obtained outcomes indicate that the BDE-BSS-DR outperforms the BDE and BDE-BSS algorithms significantly. Furthermore, the effectiveness of the proposed algorithms in selecting the most important features of the heart disease data, several cancer diseases, and COVID-19 are also compared with several other state-of-the-art methods. Our results show that the BDE-BSS-DR with SVM classifier has a significant advantage over other methods with an average classification accuracy of 95.05% in heart disease and 99.40% in COVID-19 disease. In addition, the comparisons made with KNN and SVM classification prove the efficiency of the DR and BSS in generating a subset of optimal and informative features.

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Nowadays, commercial fraud behaviors commonly occur in many industries. However, due to obstacles like concept drift, imbalanced dataset and uneven distribution of fraud entries, Fraud Detection System (FDS) fails to identify such behaviors. Among the problems mentioned above, most research focus on dealing with skewed dataset. This paper first presents common application scenarios of FDS which consist of credit card fraud, insurance fraud and supply chain fraud. Then, this study introduces five representative methods in dealing with problems mentioned above, which are K Nearest Neighbors-Synthetic Minority Oversampling Technique-Long Short-term Memory Networks (kNN-SMOTE-LSTM), Generative Adversarial Nets-AdaBoost-Decision tree (GAN-AdaBoost-DT), Wasserstein GAN-Kernel Density Estimation-Gradient Boosting DT (WGAN-KDE-GBDT), Time-LSTM (TLSTM) and Adaptive Synthetic Sampling-Sequential Forward Selection-Random Forest (ADASYN-SFS-RF). KNN-SMOTE-LSTM adopts KNN as an identifying classifier so as to only retain true samples. GAN-AdaBoost-DT generates new samples without referring to real transactions. WGAN-KDE-GBDT uses Wasserstein Distance as distance measurement instead, and thus improves training speed and guarantees successful generation. TLSTM tires to consider the weights of different time intervals and measures the similarity between the simulated behavior and the genuine behavior. ADASYN-SFS-RF employs SFS algorithm, basing on RF, to only reserve optimal subsets of features. Finally, result metrics prove that those improved algorithms do improve the overall performance of FDS, even if with limitations at some indicators.

Golden jackal optimization algorithm with deep learning assisted intrusion detection system for network security

Network security is essential to our daily communications and networks. Cybersecurity researchers initiate the significance of emerging proficient network intrusion detection systems (IDS) for providing secure networks. While attackers endure to progress novel kinds of attacks and the network sizes endure to develop, the necessity for effectual IDS becomes significant. Additionally, IDS aims to offer confidentiality, integrity, and availability for the data communicated in networked computers by avoiding illegal access to the network. Several studies executed machine learning (ML) systems for emerging effectual IDS; then, with the advent of deep learning (DL) systems and artificial neural networks (ANNs) that create features automatically without human interference, researchers started to depend on DL approaches. This study introduces a new Golden Jackal Optimization Algorithm with Deep Learning Assisted Intrusion Detection System for Network Security (GJOADL-IDSNS) technique. The major intention of the GJOADL-IDSNS system lies in the effectual recognition and classification of the intrusions, to achieve network security. Primarily, data normalization is performed to scale the input data into a useful format. In the presented GJOADL-IDSNS technique, the GJOA-based feature selection (GJOA-FS) approach can be employed to elect an optimum subset of features. Next, the GJOADL-IDSNS methodology applies the attention-based bi-directional long short-term memory (A-BiLSTM) model. For hyperparameter tuning of the A-BiLSTM model, the GJOADL-IDSNS technique uses the salp swarm algorithm (SSA). The simulation value of the GJOADL-IDSNS technique has been tested utilizing benchmark datasets. The comparative results stated that the GJOADL-IDSNS technique achieves better performance than other models.

Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases

Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, , and Recall, confirming the effectiveness of the proposed method.

An integrated feature selection approach to high water stress yield prediction.

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

SFE: A Simple, Fast, and Efficient Feature Selection Algorithm for High-Dimensional Data

In this paper, a new feature selection algorithm, called SFE (Simple, Fast, and Efficient), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: non-selection and selection. It comprises two phases: exploration and exploitation. In the exploration phase, the non-selection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features, and changes the status of the features from selected mode to non-selected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results, and changes the status of the features from non-selected mode to selected mode. The proposed SFE is successful in feature selection from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this paper proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for feature selection are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed feature selection algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.

The value of CT-based radiomics in predicting the prognosis of acute pancreatitis

PurposeEarly judgment of the progress of acute pancreatitis (AP) and timely intervention are crucial to the prognosis of patients. The purpose of this study was to investigate the application value of CT-based radiomics of pancreatic parenchyma in predicting the prognosis of early AP.Materials and methodsThis retrospective study enrolled 137 patients diagnosed with AP (95 cases in the progressive group and 42 cases in the non-progressive group) who underwent CT scans. Patients were randomly divided into a training set (n = 95) and a validation set (n = 42) in a ratio of 7: 3. The region of interest (ROI) was outlined along the inner edge of the pancreatic parenchyma manually, and the Modified CT Severity Index (MCTSI) was assessed. After resampling and normalizing the CT image, a total of 2,264 radiomics features were extracted from the ROI. The radiomics features were downscaled and filtered using minimum redundancy maximum correlation (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression, in turn, and the more optimal subset of radiomics features was selected. In addition, the radiomics score (rad-score) was calculated for each patient by the LASSO method. Clinical data were also analyzed to predict the prognosis of AP. Three prediction models, including clinical model, radiomics model, and combined clinical–radiomics model, are constructed. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curve analysis. The DeLong test was employed to compare the differences between the ROC curves. The decision curve analysis (DCA) is used to assess the net benefit of the model.ResultsThe mRMR algorithm and LASSO regression were used to select 13 radiomics features with high values. The rad-score of each texture feature was calculated to fuse MCTSI to establish the radiomics model, and both the clinical model and clinical–radiomics model were established. The clinical–radiomics model showed the best performance, the AUC and 95% confidence interval, accuracy, sensitivity, and specificity of the clinical–radiomics model in the training set were 0.984 (0.964–1.000), 0.947, 0.955, and 0.931, respectively. In the validation set, they were 0.942 (0.870–1.000), 0.929, 0.966, and 0.846, respectively. The Delong test showed that the predictive efficacy of the clinical–radiomics model was higher than that of the clinical model (Z = 2.767, p = 0.005) and the radiomics model (Z = 2.033, p = 0.042) in the validation set. Decision curve analysis demonstrated higher net clinical benefit for the clinical–radiomics model.ConclusionThe pancreatic parenchymal CT clinical–radiomics model has high diagnostic efficacy in predicting the progression of early AP patients, which is significantly better than the clinical or radiomics model. The combined model can help identify and determine the progression trend of patients with AP and improve the prognosis and survival of patients as early as possible.

Prediction and customized design of Curie temperature of Fe-based amorphous alloys based on interpretable machine learning

Predicting the Curie temperature (Tc) is a crucial problem in the field of amorphous alloys. In this study, Fe-based amorphous alloys are taken as an example, and the composition and corresponding Tc are collected through a literature review. Three feature construction strategies are employed to establish the relationship between the composition and Tc using machine learning. The research findings demonstrate that the combination of the Meredig rule and the GBT algorithm yields the highest prediction accuracy. The features are constructed using recursive elimination and enumeration methods, ultimately resulting in an optimal 8-dimensional feature subset. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to analyze the interpretability of the prediction model, providing a feature importance ranking and their critical values. Finally, by replacing the Fe and P atoms with Mn and Si atoms, respectively, in the Fe80P13C7 alloy, Fe62Mn18P(13-x)C7Six (x = 4,5,6) alloys are successfully designed to exhibit a Tc close to room temperature (335 K), enabling customized Tc design for Fe-based amorphous alloys.

Computer Hardware Fault Detection based on Machine Learning

In order to solve the computer fault detection problem of machine learning, the author proposes a computer hardware fault detection problem based on machine learning. The method combines mutual information and class separability to analyze their relationship, which improves classification accuracy. This study presents an adaptive machine learning technique for the adaptive fusion of data from multiple sources. In addition, the mCRC algorithm seeks for the optimal feature subset using the enhanced forward floating search method, thereby overcoming the limitation that the mRMR algorithm does not specify how to determine the final feature subset. The classification accuracy of the mCRC algorithm is approximately 1% better than that of the mRMR algorithm, and the size of the final feature subset of the mCRC algorithm is 22% smaller than that of the final subset of the mRMR algorithm. Conclusion: the ReMAE algorithm has a higher rate of accurate failure prediction.

Computed tomography and guidelines-based human-machine fusion model for predicting resectability of the pancreatic cancer.

The study aims to develop a hybrid machine learning model for predicting resectability of the pancreatic cancer, which is based on computed tomography (CT) and National Comprehensive Cancer Network (NCCN) guidelines. We retrospectively studied 349 patients. One hundred seventy-one cases from Center 1 and 92 cases from Center 2 were used as the primary training cohort, and 66 cases from Center 3 and 20 cases from Center 4 were used as the independent test dataset. Semi-automatic module of ITK-SNAP software was used to assist CT image segmentation to obtain three-dimensional (3D) imaging region of interest (ROI). There were 788 handcrafted features extracted for 3D ROI using PyRadiomics. The optimal feature subset consists of three features screened by three feature selection methods as the input of the SVM to construct the conventional radiomics-based predictive model (cRad). 3D ROI was used to unify the resolution by 3D spline interpolation method for constructing the 3D tumor imaging tensor. Using 3D tumor image tensor as input, 3D kernelled support tensor machine-based predictive model (KSTM), and 3D ResNet-based deep learning predictive model (ResNet) were constructed. Multi-classifier fusion ML model is constructed by fusing cRad, KSTM, and ResNet using multi-classifier fusion strategy. Two experts with more than 10years of clinical experience were invited to reevaluate each patient based on their CECT following the NCCN guidelines to obtain resectable, unresectable, and borderline resectable diagnoses. The three results were converted into probability values of 0.25, 0.75, and 0.50, respectively, according to the traditional empirical method. Then it is used as an independent classifier and integrated with multi-classifier fusion machine learning (ML) model to obtain the human-machine fusion ML model (HMfML). Multi-classifier fusion ML model's area under receiver operating characteristic curve (AUC; 0.8610), predictive accuracy (ACC: 80.23%), sensitivity (SEN: 78.95%), and specificity (SPE: 80.60%) is better than cRad, KSTM, and ResNet-based single-classifier models and their two-classifier fusion models. This means that three different models have mined complementary CECT feature expression from different perspectives and can be integrated through CFS-ER, so that the fusion model has better performance. HMfML's AUC (0.8845), ACC (82.56%), SEN (84.21%), SPE (82.09%). This means that ML models might learn extra information from CECT that experts cannot distinguish, thus complementing expert experience and improving the performance of hybrid ML models. HMfML can predict PC resectability with high accuracy.

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature selection methods: t-test and sequential forward floating selection (SFFS). The subset of selected features was presented to the k-nearest neighbor classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.

Enhancing the feature selection by employing improved optimization with Simulated Annealing Algorithm for dimensionality reduction in intrusion detection dataset

In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity.

A data-driven operating improvement method for the thermal power unit with frequent load changes

In the near and medium term, thermal power generation will still play an important peak-shaving role in providing grid connection to volatile renewable energy. Thermal power units need to adjust from the current load to the given load within a given time period, which provides space for operational improvement. In this paper, we propose an operating improvement method for the thermal power unit based on real-time monitoring data by observation dividing, feature construction and selection, clustering, and machine learning. The unit operation data is categorized into three feature subsets based on domain knowledge, which is used to distinguish different functions of historical data. Subsequently, the optimal feature subset and observations are selected for building regression models, including linear regression (LR), ensemble tree regression (ETR), neural network regression (NNR), and support vector regression (SVR). R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE) are adopted to test the performance of the proposed regression model on the real-time monitoring data of a thermal unit, which has 80,000 observations of 36 different variables. Compared with benchmark methods, the proposed method has lower regression error in the numerical experiment. Thus, we can thereby improve the efficiency of operational management based on the built learning model. Furthermore, in response to peak shaving requirements, the proposed method in this article considers operational optimization over time periods containing multiple time points compared to the traditional operational optimization perspective. With the continuous arrival of monitoring data, the above method can be updated in a timely manner based on the new database to address model adjustments caused by unit aging and other factors.

Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology.

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.

Maximum margin and global criterion based-recursive feature selection

In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the feature ranking criterion utilized in these approaches is inconsistent with the maximum-margin theory. Secondly, the computation of the criterion is performed locally, lacking the ability to measure the importance of features globally. To overcome these challenges, we propose a novel feature ranking criterion called Maximum Margin and Global (MMG) criterion. This criterion utilizes the classification margin to determine the importance of features and computes it globally, enabling a more accurate assessment of feature importance. Moreover, we introduce an optimal feature subset evaluation algorithm that leverages the MMG criterion to determine the best subset of features. To enhance the efficiency of the proposed algorithms, we provide two alpha seeding strategies that significantly reduce computational costs while maintaining high accuracy. These strategies offer a practical means to expedite the feature selection process. Through extensive experiments conducted on ten benchmark datasets, we demonstrate that our proposed algorithms outperform current state-of-the-art methods. Additionally, the alpha seeding strategies yield significant speedups, further enhancing the efficiency of the feature selection process.

A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection

In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.