Feature Selection Approach Research Articles

Implementation of Chernobyl optimization algorithm based feature selection approach to predict software defects

Background Software defects can have catastrophic consequences. Therefore, fixing these defects is crucial for the evolution of software. Software Defect Prediction (SDP) enables developers to investigate unscramble faults in the inaugural parts of the software progression mechanism. However, SDP faces many challenges, including the high magnitude of attributes in the datasets, which can degrade the prognostic performance of a defect forecasting model. Feature selection (FS), a compelling instrument for overcoming high dimensionality, selects only the relevant and best features while carefully discarding others. Over the years, several meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have been used to develop defect prediction models. However, these models suffer from several drawbacks, such as high cost, local optima trap, lower convergence rate, and higher parameter tuning. To overcome the above shortcomings, this study aims to develop an innovative FS technique, namely, the Chernobyl Optimization Algorithm (FSCOA), to unwrap the most informative features that can produce a precise prediction model while minimizing errors. Methods The proposed FSCOA approach mimicked the process of nuclear radiation while attacking humans after an explosion. The proposed FSCOA approach was combined with four widely used classifiers, namely Decision Tree (DT), K-nearest neighbor (KNN), Naive Bayes (NB), and Quantitative Discriminant Analysis (QDA), to determine the finest attributes from the SDP datasets. Furthermore, the accuracy of the recommended FSCOA method is correlated with existing FS techniques, such as FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experimental findings showed that the proposed FSCOA approach yielded the best accuracy in most cases and achieved an average rank of 1.75, followed by the other studied FS approaches. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The experimental findings showed that the prospective FSCOA procedure eclipsed alternative FS techniques with higher accuracy in almost all cases while selecting optimal features.

A New Performance Metric to Evaluate Filter Feature Selection Methods in Text Classification

High dimensionality and sparsity are the primary issues in text classification. Using feature selection approaches, the most effective way to solve the problem is to select a subset of features. The most common and effective methods used for this process are filter techniques. Various performance metrics such as Micro-F1, Macro-F1, and Accuracy are used to evaluate the performance of filter methods used for feature selection on datasets  Such methods work depending on a classification algorithm. However, when selecting features in filter techniques, the information on the individual features is evaluated without considering the relationship between the features. In such an approach, the actual performance of the filter technique used in feature selection may not be determined. In such a case, it causes the existing methods to be insufficient in testing the validity of the proposed method. For this purpose, this study suggests a novel performance metric called Selection Error (SE) to determine the actual performance evaluation of filter techniques. The Selection Error metric allows us to analyze the information value of the selected features more accurately than existing methods without relying on a classifier. The feature selection performance of the filtering approaches was performed on six different datasets with both The Selection Error and traditional performance metrics. When the results are examined, it is seen that there is a strong relationship between the proposed performance metric and the classification performance metric results. The Selection Error aims to significantly contribute to the literature by demonstrating the success of filtering feature selection methods, regardless of classifier performance. 

A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data

A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data

A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury

Acute kidney injury (AKI) is one of the most important lethal factors for patients admitted to intensive care units (ICUs), and timely high-risk prognostic assessment and intervention are essential to improving patient prognosis. In this study, a stacking model using the MIMIC-III dataset with a two-tier feature selection approach was developed to predict the risk of in-hospital mortality in ICU patients admitted for AKI. External validation was performed using separate MIMIC-IV and eICU-CRD. The area under the curve (AUC) was calculated using the stacking model, and features were selected using the Boruta and XGBoost feature selection methods. This study compares the performance of a stacking model using two-tier feature selection with a model using single-tier feature selection (XGBoost: 85; Boruta: 83; two-tier: 0.91). The predictive effectiveness of the stacking model was further validated by using different datasets (Validation 1: 0.83; Validation 2: 0.85) and comparing it with a simpler model and traditional clinical scores (SOFA: 0.65; APACH IV: 0.61). In addition, this study combined interpretable techniques and causal inference to analyze the causal relationship between features and predicted outcomes.

Development and validation of a GBDT-based for detection of diabetes

Improved early detection and intervention can lessen the substantial health and financial burden of type 2 diabetes (T2D). In order to achieve early prediction of diabetes, an approach for diabetes prediction based on Gradient Boosting Decision Tree (GBDT) was proposed in the present study. The feature selection approach based on GBDT was designed to identify the most relevant features, where GBDT builds a powerful model by combining multiple weak classifiers, which can reduce the risk of overfitting and improve the model generalization ability. Condition prediction was performed based on GBDT and the filtered features, and the truncation value of the feature was calculated. The experimental results in the paper showed that the Area Under the Curve (AUC) value of GBDT was 0.9788, which was a big improvement compared with other studies; the AUC value based on glycated hemoglobin level was 0.7307, and the cutoff value of glycated hemoglobin level was about 6.8, which was very accurate. The prediction of diabetes based on GBDT can help patients to understand whether they have diabetes initially based on their own glycated hemoglobin values, and it can also help clinicians to make more objective judgments in clinical diagnosis in order to judge the patient's situation and subsequent monitoring of the condition, making an excellent contribution to the control of the patient's condition.

Microbiome variations induced by delta9-tetrahydrocannabinol predict weight reduction in obese mice

IntroductionObesity and high-fat diets induce consistent alterations in gut microbiota composition. Observations from epidemiological reviews and experiments also illustrate weight regulation effects of delta9-tetrahydrocannabinol (THC) with microbiome shifts. Therefore, we investigated the weight-loss potential of THC in obese mice models and to elucidate the contribution of specific gut microbiome changes in THC-induced weight loss.MethodsHigh-fat diet induced obese mice were treated with oral THC supplementation for two weeks and compared with controls. In addition to measuring weight, fecal samples were obtained at various timepoints, sequenced for bacterial 16s rRNA content and analyzed using QIIME2. Alpha and beta diversity were computed followed by linear mixed effects (LME) modeling of bacterial relative abundance relationship to THC treatment and weight change.ResultsIn both male and female mice, the THC group had significantly greater average weight loss than controls (−17.8% vs. −0.22%, p<0.001 and −13.8% vs. +2.9%, p<0.001 respectively). Male mice had 8 bacterial taxonomic features that were both significantly different in relative abundance change over time with THC and correlated with weight change. An LME model using three bacterial features explained 76% of the variance in weight change with 24% of variation explained by fixed effects of feature relative abundance alone. The model also accurately predicted weight change in a second male mouse cohort (R=0.64, R2=0.41, p=<0.001). Female mice had fewer significant predictive features and were difficult to model, but the male-produced 3-feature model still accurately predicted weight change in the females (R=0.66, R2=0.44, p<0.001).ConclusionUsing a stepwise feature selection approach, our results indicate that sex-specific gut microbiome composition changes play some role in THC-induced weight loss. Additionally, we illustrated the concept of microbiome feature-based modeling to predict weight changes.

Development and validation of a GBDT-based for detection of diabetes

Improved early detection and intervention can lessen the substantial health and financial burden of type 2 diabetes (T2D). In order to achieve early prediction of diabetes, an approach for diabetes prediction based on Gradient Boosting Decision Tree (GBDT) was proposed in the present study. The feature selection approach based on GBDT was designed to identify the most relevant features, where GBDT builds a powerful model by combining multiple weak classifiers, which can reduce the risk of overfitting and improve the model generalization ability. Condition prediction was performed based on GBDT and the filtered features, and the truncation value of the feature was calculated. The experimental results in the paper showed that the Area Under the Curve (AUC) value of GBDT was 0.9788, which was a big improvement compared with other studies; the AUC value based on glycated hemoglobin level was 0.7307, and the cutoff value of glycated hemoglobin level was about 6.8, which was very accurate. The prediction of diabetes based on GBDT can help patients to understand whether they have diabetes initially based on their own glycated hemoglobin values, and it can also help clinicians to make more objective judgments in clinical diagnosis in order to judge the patient's situation and subsequent monitoring of the condition, making an excellent contribution to the control of the patient's condition.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

ObjectivesLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis. Materials and methodsWe propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost. ResultsThe proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes. ConclusionIn summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Computational Cost Reduction in Multi-Objective Feature Selection Using Permutational-Based Differential Evolution

Feature selection is a preprocessing step in machine learning that aims to reduce dimensionality and improve performance. The approaches for feature selection are often classified according to the evaluation of a subset of features as filter, wrapper, and embedded approaches. The high performance of wrapper approaches for feature selection is associated at the same time with the disadvantage of high computational cost. Cost-reduction mechanisms for feature selection have been proposed in the literature, where competitive performance is achieved more efficiently. This work applies the simple and effective resource-saving mechanisms of the fixed and incremental sampling fraction strategies with memory to avoid repeated evaluations in multi-objective permutational-based differential evolution for feature selection. The selected multi-objective approach is an extension of the DE-FSPM algorithm with the selection mechanism of the GDE3 algorithm. The results showed high resource savings, especially in computational time and the number of evaluations required for the search process. Nonetheless, it was also detected that the algorithm’s performance was diminished. Therefore, the results reported in the literature on the effectiveness of the strategies for cost reduction in single-objective feature selection were only partially sustained in multi-objective feature selection.

On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.

DeepPIG: deep neural network architecture with pairwise connected layers and stochastic gates using knockoff frameworks for feature selection

Selecting relevant feature subsets is essential for machine learning applications. Among the feature selection techniques, the knockoff filter procedure proposes a unique framework that minimizes false discovery rates (FDR). However, employing a deep neural network architecture for a knockoff filter framework requires higher detection power. Using the knockoff filter framework, we present a Deep neural network with PaIrwise connected layers integrated with stochastic Gates (DeepPIG) for the feature selection model. DeepPIG exhibited better detection power in synthetic data than the baseline and recent models such as Deep feature selection using Paired-Input Nonlinear Knockoffs (DeepPINK), Stochastic Gates (STG), and SHapley Additive exPlanations (SHAP) while not violating the preselected FDR level, especially when the signal of the features were weak. The selected features determined by DeepPIG demonstrated superior classification performance compared with the baseline model in real-world data analyses, including the prediction of certain cancer prognosis and classification tasks using microbiome and single-cell datasets. In conclusion, DeepPIG is a robust feature selection approach even when the signals of features are weak. Source code is available at https://github.com/DMCB-GIST/DeepPIG.

Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio–visual content using EEG

Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio–visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio–visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.

Homogeneous Ensemble Feature Selection for Mass Spectrometry Data Prediction in Cancer Studies

The identification of important proteins is critical for the medical diagnosis and prognosis of common diseases. Diverse sets of computational tools have been developed for omics data reduction and protein selection. However, standard statistical models with single-feature selection involve the multi-testing burden of low power with limited available samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning (ML) may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection (HEFS) approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics datasets comprising (1) binary putative homologous recombination deficiency (HRD)- positive or -negative samples; (2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown samples). We conducted and compared various ML methods with HEFS including random forest (RF), support vector machine (SVM), and neural network (NN) for predicting both binary and multiple-class outcomes. The results indicated that the prediction accuracies varied for both binary and multiple-class classifications using various ML approaches with the proposed HEFS method. RF and NN provided better prediction accuracies than simple Naive Bayes or logistic models. For binary outcomes, with a sample size of 122 and nine selected prediction proteins using our proposed three-stage HEFS approach, the best ensemble ML (Treebag) achieved 83% accuracy, 85% sensitivity, and 81% specificity. For multiple (five)-class outcomes, the proposed HEFS-selected proteins combined with Principal Component Analysis (PCA) in NN resulted in prediction accuracies for multiple-class classifications ranging from 75% to 96% for each of the five classes. Despite the different prediction accuracies of the various models, HEFS identified consistent sets of proteins linked to the binary and multiple-class outcomes.

Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.

Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.

An improved long short-term memory with denoising autoencoder for solving text classification problems

In the past decade, text classification has been a popular research field for automatically categorizing documents into relevant categories. Predicting classes for input samples based on their features is the goal of classification. To limit the dimension of the feature space for precise text categorization, feature selection approaches are widely used for recognizing important and affective features while ignoring unimportant, weak, and chaotic components. This paper proposes a new model referred to as DAE-LSTM for reducing data dimension through the use of a denoising autoencoder. (Long Short-Term Memory (LSTM) is one of the popular text classification approaches, particularly when dealing with sequential datasets. Additionally, the Rectified Linear Unit (Relu) activation function was used instead of the hyperbolic tangent activation function (tanh) to improve the accuracy of the model. Four benchmark datasets were used to evaluate the proposed model against 4 cutting-edge text classification methods, including conventional Bidirectional LSTM, Bidirectional GRU, CNN-LSTM, and CNN-GRU. The suggested model has been found to perform much better than current methods using several kinds of performance assessment measures.

Minimizing Survey Questions for PTSD Prediction Following Acute Trauma.

Traumatic experiences have the potential to give rise to post-traumatic stress disorder (PTSD), a debilitating psychiatric condition associated with impairments in both social and occupational functioning. There has been great interest in utilizing machine learning approaches to predict the development of PTSD in trauma patients from clinician assessment or survey-based psychological assessments. However, these assessments require a large number of questions, which is time consuming and not easy to administer. In this paper, we aim to predict PTSD development of patients 3 months post-trauma from multiple survey-based assessments taken within 2 weeks post-trauma. Our objective is to minimize the number of survey questions that patients need to answer while maintaining the prediction accuracy from the full surveys. We formulate this as a feature selection problem and consider 4 different feature selection approaches. We demonstrate that it is possible to achieve up to 72% accuracy for predicting the 3-month PTSD diagnosis from 10 survey questions using a mean decrease in impurity-based feature selector followed by a gradient boosting classifier.

Fast Sparse Discriminative K-Means for Unsupervised Feature Selection.

Embedded feature selection approach guides subsequent projection matrix (selection matrix) learning through the acquisition of pseudolabel matrix to conduct feature selection tasks. Yet the continuous pseudolabel matrix learned from relaxed problem based on spectral analysis deviates from reality to some extent. To cope with this issue, we design an efficient feature selection framework inspired by classical least-squares regression (LSR) and discriminative K-means (DisK-means), which is called the fast sparse discriminative K-means (FSDK) for the feature selection method. First, the weighted pseudolabel matrix with discrete trait is introduced to avoid trivial solution from unsupervised LSR. On this condition, any constraint imposed into pseudolabel matrix and selection matrix is dispensable, which is significantly beneficial to simplify the combinational optimization problem. Second, the l2,p -norm regularizer is introduced to satisfy the row sparsity of selection matrix with flexible p . Consequently, the proposed FSDK model can be treated as a novel feature selection framework integrated from the DisK-means algorithm and l2,p -norm regularizer to optimize the sparse regression problem. Moreover, our model is linearly correlated with the number of samples, which is speedy to handle the large-scale data. Comprehensive tests on various data terminally illuminate the effectiveness and efficiency of FSDK.

A novel IMW-mrmr feature selection approach for early detection of hypertension with HRV analysis

A novel IMW-mrmr feature selection approach for early detection of hypertension with HRV analysis