Wrapper Feature Selection Method Research Articles

LLpowershap: logistic loss-based automated Shapley values feature selection method

BackgroundShapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.MethodsWe present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing.ResultsOur simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets.ConclusionOur results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

A wrapper feature selection approach using Markov blankets

In feature selection, Markov Blanket (MB) based approaches have attracted considerable attention with most MB discovery algorithms being categorized as filter based techniques. Typically, the Conditional Independence (CI) test employed by such methods is different for different data types. In this article, we propose a novel Markov Blanket based wrapper feature selection method. The proposed approach employs Predictive Permutation Independence (PPI), a novel Conditional Independence (CI) test that allows it to work out-of-the-box for both classification and regression tasks on mixed data. PPI can work with any supervised algorithm to estimate the association of a feature with the target variable while also providing a measure of feature importance. The proposed approach also includes an optional MB aggregation step that can be used to find the optimal MB under non-faithful conditions. Our method11Implementation and experimental results are available at https://github.com/AnonymousMLSubmissions/PatternRecognitionSubmission. outperforms other MB discovery methods, in terms of F1-score, by 7% on average, over 3 large-scale BN datasets. It also outperforms state-of-the-art feature selection techniques on 13 real-world datasets.

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

A Machine Learning-Based Wrapper Method for Feature Selection

This paper presents a two-stage feature selection scheme using machine learning techniques. In the first stage a wrapper method is adopted to select various combinations of subsets of features from the original dataset. The performance of the model is evaluated by three classifiers: K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF). In the second and final stage, a sequential backward feature selection Method is applied. The proposed method is demonstrated on eighteen datasets and the average classification accuracy of eighteen datasets achieved is 89.81%, 87.55%, and 89.82% using the KNN, SVM, and RF classifiers, respectively with a maximum reduced size of the subset being ten only. Comparing the proposed method to eight other feature selection methods, the former achieves better classification accuracy in terms of selecting the most useful but a smaller number of features.

Optimizing Fall Risk Diagnosis in Older Adults Using a Bayesian Classifier and Simulated Annealing.

The aim of this study was to improve the diagnostic ability of fall risk classifiers using a Bayesian approach and the Simulated Annealing (SA) algorithm. A total of 47 features from 181 records (40 Center of Pressure (CoP) indices and 7 patient descriptive variables) were analyzed. The wrapper method of feature selection using the SA algorithm was applied to optimize the cost function based on the difference of the mean minus the standard deviation of the Area Under the Curve (AUC) of the fall risk classifiers across multiple dimensions. A stratified 60-20-20% hold-out method was used for train, test, and validation sets, respectively. The results showed that although the highest performance was observed with 31 features (0.815 ± 0.110), lower variability and higher explainability were achieved with only 15 features (0.780 ± 0.055). These findings suggest that the SA algorithm is a valuable tool for feature selection for acceptable fall risk diagnosis. This method offers an alternative or complementary resource in situations where clinical tools are difficult to apply.

Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis

As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model’s predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance.

Financial distress prediction using an improved particle swarm optimization wrapper feature selection method and tree boosting ensemble

Financial distress prediction (FDP) is a complex task involving both feature selection and model construction. While many studies have addressed these challenges individually, there is a lack of FDP models that integrate feature selection into the overall model building process. To address the issues of fast convergence and local optimization in particle swarm optimization (PSO), this study proposes a modified PSO algorithm with three improvement strategies. This improved PSO, known as IPSO, is embedded within state-of-the-art tree boosting ensemble models for feature selection. IPSO continuously evolves toward the optimal feature subset, preventing premature convergence and avoiding local optima. To construct the FDP model, four state-of-the-art tree boosting models are combined using an improved majority voting ensemble strategy. The integration of IPSO enhances the prediction performance of the model. Comparative experiments are conducted on samples of Chinese listed companies under short-term and long-term prediction scenarios to evaluate the proposed model. The results demonstrate that IPSO significantly improves the prediction performance, and the ensemble strategy enhances the robustness of the predictions.

Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach

The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.

Optimizing prediction accuracy for early recurrent lumbar disc herniation with a directional mutation-guided SVM model

Percutaneous endoscopic lumbar discectomy (PELD) is one of the main means of minimally invasive spinal surgery, and is an effective means of treating lumbar disc herniation, but its early recurrence is still difficult to predict. With the development of machine learning technology, the auxiliary model based on the prediction of early recurrent lumbar disc herniation (rLDH) and the identification of causative risk factors have become urgent problems in current research. However, the screening ability of current models for key factors affecting the prediction of rLDH, as well as their predictive ability, needs to be improved. Therefore, this paper presents a classification model that utilizes wrapper feature selection, developed through the integration of an enhanced bat algorithm (BDGBA) and support vector machine (SVM). Among them, BDGBA increases the population diversity and improves the population quality by introducing directional mutation strategy and guidance-based strategy, which in turn allows the model to secure better subsets of features. Furthermore, SVM serves as the classifier for the wrapper feature selection method, with its classification prediction results acting as a fitness function for the feature subset. In the proposed prediction method, BDGBA is used as an optimizer for feature subset filtering and as an objective function for feature subset evaluation based on the classification results of the support vector machine, which improves the interpretability and prediction accuracy of the model. In order to verify the performance of the proposed method, this paper proves the performance of the model through global optimization experiments and prediction experiments on real data sets. The accuracy of the proposed rLDH prediction model is 93.49% and sensitivity is 88.33%. The experimental results show that Level of herniated disk, Modic change, Disk height, Disk length, and Disk width are the key factors for predicting rLDH, and the proposed method is an effective auxiliary diagnosis method.

Deep Curious Feature Selection: A Recurrent, Intrinsic-Reward Reinforcement Learning Approach to Feature Selection

Feature selection (FS) is an important step in the process of building machine learning-based models. The goal of the feature selection step is to find a small subset of features that will provide good prediction results by removing noisy, irrelevant, or repetitive features. Commonly used Wrapper methods use a machine learning model as a black box and its performance as the goal function for evaluating different features' subsets and selecting the best one. To avoid examining all possible subsets (an NP-hard problem), search algorithms are used to find the subsets to be examined, in a heuristic way. As exhaustive search methods are computationally complicated, most methods use simple and greedy search methods that yield only locally optimal results and are not sensitive to possible features interactions, which means that a feature may be chosen at the expense of two others that are more informative together. We analyze the problem of searching the features subset space with reference to two dimensions, namely, memory of past selected features subset and future selected features. We propose a new wrapper feature selection method based on the deep artificial curiosity framework, which implements intrinsic reward reinforcement learning, with long short-term memory unit (LSTM). This novel algorithm integrates these two elements of memory and future step. We show that our method, called the Deep Curious Feature Selection (DCFS) algorithm, deals with feature interactions, and provides a feature subset which improves the accuracy of learning models on artificial and real datasets.

Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection

Intrusion detection systems play a critical role in the mitigation of cyber-attacks on the Internet of Things (IoT) environment. Due to the integration of many devices within the IoT environment, a huge amount of data is generated. The generated data sets in most cases consist of irrelevant and redundant features that affect the performance of the existing intrusion detection systems (IDS). The selection of optimal features plays a critical role in the enhancement of intrusion detection systems. This study proposes a sequential feature selection approach using an optimized extreme learning machine (ELM) with an SVM (support vector machine) classifier. The main challenge of ELM is the selection of the input parameters, which affect its performance. In this study, the genetic algorithm (GA) is used to optimize the weights of ELM to boost its performance. After the optimization, the algorithm is applied as an estimator in the sequential forward selection (wrapper technique) to select key features. The final obtained feature subset is applied for classification using SVM. The IoT_ToN network and UNSWNB15 datasets were used to test the model's performance. The performance of the model was compared with other existing state-of-the-art classifiers such as k-nearest neighbors, gradient boosting, random forest, and decision tree. The model had the best quality of the selected feature subset. The results indicate that the proposed model had a better intrusion detection performance with 99%, and 86% accuracy for IoT_ToN network dataset and UNSWNB15 datasets, respectively. The model can be used as a promising tool for enhancing the classification performance of IDS datasets.

Embedded Feature Selection Approach Using Penalized Logistic Regression for Universal Steganalysis

For universal steganalysis, the Spatial Rich Model (SRM) provides a robust feature set in the form of 106 feature submodels (34671-dimensional) to attack highly secure steganography methods. Nonetheless, the curse of dimensionality emerges as a drawback, prompting researchers to explore feature selection methods to achieve optimal performance for steganalyzers. In previous work, a wrapper feature selection method using global and local best particle swarm optimization (GLBPSO) has been used to select 28 submodels of SRM out of 106 submodels. The 28 submodels contain a total of 9152 features and an ensemble classifier has been used for the classification results. In this study, an embedded feature selection and classification approach using logistic regression (LR) with an elastic net penalty has been proposed and applied to the 9152 features. The proposed approach efficiently selects an informative subset of 9152 features while training the LR classifier. This not only reduces the number of features by a significant 75% but also boosts the classification performance when contrasted with the ensemble classifier. The proposed approach achieves an accuracy of 87.41%, surpassing the current state-of-the-art steganalysis methods based on deep learning by a substantial margin of 3-7%. It also outperforms a recent machine learning-based steganalysis method by 13%.

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories—signal property, inter-/intra-position correlation, peak value/interval variability, and demographics—were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

Hybrid distributed feature selection using particle swarm optimization-mutual information

Feature selection (FS) is a data preprocessing step in machine learning (ML) that selects a subset of relevant and informative features from a large feature pool. FS helps ML models improve their predictive accuracy at lower computational costs. Moreover, FS can handle the model overfitting problem on a high-dimensional dataset. A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets. The proposed “HDFS(PSO-MI): hybrid distribute feature selection using particle swarm optimization-mutual information (PSO-MI)”, is a PSO-based hybrid method that can overcome the problem mentioned above. This method hybridizes the filter and wrapper techniques in a distributed manner. A new combiner is also introduced to merge the effective features selected from multiple data distributions. The effectiveness of the proposed HDFS(PSO-MI) method is evaluated using five ML classifiers, i.e., logistic regression (LR), k-NN, support vector machine (SVM), decision tree (DT), and random forest (RF), on various datasets in terms of accuracy and Matthew’s correlation coefficient (MCC). From the experimental analysis, we observed that HDFS(PSO-MI) method yielded more than 98%, 95%, 92%, 90%, and 85% accuracy for the unbalanced, kidney disease, emotions, wafer manufacturing, and breast cancer datasets, respectively. Our method shows promising results comapred to other methods, such as mutual information, gain ratio, Spearman correlation, analysis of variance (ANOVA), Pearson correlation, and an ensemble feature selection with ranking method (EFSRank).

Improved feature selection using a hybrid side-blotched lizard algorithm and genetic algorithm approach

<span lang="EN-US">Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of side-blotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.</span>

End-to-end lightweight berry number prediction for supporting table grape cultivation

The advent of smart agriculture has revolutionized and streamlined various manual tasks in grape cultivation, one of which is berry thinning. This task necessitates experienced farmers to selectively remove a specific number of berries from the working bunch, as guided by the remaining number of berries in the bunch. In response, this paper introduces a novel real-time edge computing application that automates the process of counting the berries in a working bunch using a single 2D image. The proposed application employs YOLOv5-based object detection techniques (Jocher, 2021) to distinguish each working bunch and the visible and slightly occluded berries contained therein. The key contribution of this paper is to accurately predict the number of berries in the whole bunches including those not visible in a 2D image by harnessing the output from object detection to devise features based solely on bounding box information. In addition, the feature set is optimized by employing a wrapper feature selection method (Kohavi & John, 1997), in consideration of the limitations of edge computing devices. The eight selected features yield a mean absolute error (MAE) of 2.60 berries, tested on a dataset of 26,230 images. Only a slight increase over the initial 19-feature set, which achieved an MAE of 2.42 berries. Furthermore, the proposed approach has been successfully implemented and tested on an Android smartphone, the Sony Xperia 1 III, without the need for an internet connection. The overall computation time per image stands at an average of 0.333 s, confirming its potential for real-world application.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data.

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Prediction of Pavement Overall Condition Index Based on Wrapper Feature-Selection Techniques Using Municipal Pavement Data

Municipalities make significant efforts with limited resources to collect pavement condition data. Overall Condition Index (OCI), which uses the pavement surface evaluation and rating manual to identify roadways needing repair, is a convenient and common way of pavement condition assessment. Many data used in assessing the OCI are collected from fieldwork. Some data features give little insight into road conditions, and one feature may provide similar information to another; thus, effective data-collection resources can be optimized by selecting which data feature to keep and which to discard. In addition, the OCI reflects how local agencies highlight the important variables driving their pavement-management investments. It is also a reflection of the triggers that they use to propose various treatment strategies. This research aimed to evaluate pavement distresses in West Des Moines, Iowa, using machine-learning methods, and determine which combination of distresses and their distress proportions can accurately predict the OCI class of a particular pavement type. The wrapper feature-selection methods were used, fitting their results to classification-tree models. Automatic feature selection, using Featurewiz, was also considered to select the desired number of variables. Feature parameters were screened for OCI prediction using the mean decrease impurity, and the results could be used to model classification that may be used for year-long predictions. Results showed that an effective OCI estimate methodology could be determined with significant accuracy with fewer features.

An Empirical Evaluation of Feature Selection Stability and Classification Accuracy

The performance of inductive learners can be negatively affected by high-dimensional datasets. To address this issue, feature selection methods are used. Selecting relevant features and reducing data dimensions is essential for having accurate machine learning models. Stability is an important criterion in feature selection. Stable feature selection algorithms maintain their feature preferences even when small variations exist in the training set. Studies have emphasized the importance of stable feature selection, particularly in cases where the number of samples is small and the dimensionality is high. In this study, we evaluated the relationship between stability measures, as well as, feature selection stability and classification accuracy, using the Pearson Product-Moment Correlation Coefficient. We conducted an extensive series of experiments using five filter and two wrapper feature selection methods, three classifiers for subset and classification performance evaluation, and eight real-world datasets taken from two different data repositories. We measured the stability of feature selection methods using a total of twelve stability metrics. Based on the results of correlation analyses, we have found that there is a lack of substantial evidence supporting a linear relationship between feature selection stability and classification accuracy. However, a strong positive correlation has been observed among several stability metrics.