Hybrid-Recursive Feature Elimination for Efficient Feature Selection

Jeon, Hyelynn; Oh, Sejong

doi:10.3390/app10093211

Cited by 118 publications

(56 citation statements)

References 14 publications

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“…Feature selection is more important to select a good subset of features in many fields including finance, production, manufacturing, medicine, image processing, and biology. The recursive feature elimination (REF) is a technique for the selection of the best subset of optimal features, in the past study many researchers investigate and used [ 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. In this study, we used the recursive feature elimination (RFE) technique to select the optimal feature subset for the classification of COPD patients.…”

Section: Methodsmentioning

confidence: 99%

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

et al. 2021

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Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

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Section: Methodsmentioning

confidence: 99%

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

et al. 2021

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“…Once our algorithms have been evaluated, we perform RFE on the original dataset and compute the above metrics. Various works [77]- [80] regarding FS, have considered RFE as a benchmark FS algorithm, thereby making it a yardstick for comparison. Moreover, RFE behaves like a Hybrid FS model as it ranks the attributes based on feature importances, and then recursively eliminates the worst feature according to the ranking.…”

Section: Methods Of Analysismentioning

confidence: 99%

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Thejas¹,

Garg²,

Iyengar

et al. 2021

IEEE Access

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Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

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“…Considering HDLSS data, reducing the number of features is crucial to perform nonoverparameterized classification-based analyses [54]. Feature selection techniques can be classified into filter, wrapper, and embedded methods [53].…”

Section: Feature Selectionmentioning

confidence: 99%

Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

et al. 2021

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Statins are widely used as an effective therapy for ischemic vascular disorders and employed for primary and secondary prevention in cardiac and cerebrovascular diseases. Their hemostatic mechanism has also been shown to induce changes in cerebral blood flow that may result in neurocognitive improvement in subjects with Major Depressive Disorder. Behavioral data, various blood tests, and resting-state brain perfusion data were obtained at the start of this study and three months post-therapy from a small cohort of participants diagnosed with Major Depressive Disorder. Subjects received either rosuvastatin (10 mg) or placebo with their standard selective serotonin reuptake inhibitors therapy. At the end of the study, patients using rosuvastatin reported more positive mood changes than placebo users. However, standard statistical tests revealed no significant differences in any non-behavioral variables before and after the study. In contrast, feature selection techniques allowed identifying a small set of variables that may be affected by statin use and contribute to mood improvement. Classification models built to assess the distinguishability between the two groups showed an accuracy higher than 85% using only five selected features: two peripheral platelet activation markers, perfusion abnormality in the left inferior temporal gyrus, Attention Switching Task Reaction latency, and serum phosphorus levels. Thus, using machine learning tools, we could identify factors that may be causing self-reported mood improvement in patients due to statin use, possibly suggesting a regulatory role of statins in the pathogenesis of clinical depression.

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Hybrid-Recursive Feature Elimination for Efficient Feature Selection

Cited by 118 publications

References 14 publications

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

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