# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Classifier** & **Dataset** & **Application Domain** \\ \hline P21 [82] & KNN & GPCR-PROSITE dataset, ENZYME-PROSITE dataset. & For FS in protein function prediction. \\ \hline P22 [83] & KNN, SVM & Tumors 9, Tumors 11, Brain Tumor 1, Tumors 14, Brain Tumor 2, Leukemia 1, Leukemia 2, Lung Cancer, SRBCT, Prostate Tumor and diffuse large B-cell lymphoma datasets. & To improve gene selection in medical diagnosis. \\ \hline P23 [84] & Ionosphere, Breast Cancer Wisconsin, Connectionist Bench, Statlog, Parkinson, 9\_Tuors, Leukemia2. & To solve FS problems (Miscellaneous). \\ \hline P24 [85] & Five nearest neighbors (5-NN) & Hill-Valley, Gas 6, Musk 1, Madelon, Isolet 5, Lung. & To solve the FS problem in high-dimensional datasets (Miscellaneous). \\ \hline P25 [86] & KNN & UCI (Zoo, Breast Cancer, Breast EW, Congress EW, Exactly, Exactly 2, Heart EW, Ionosphere, KrvskpEW, Lymphography EW, M of N, Penglung EW, Sonar EW, Spect EW, Tic-tac-toe, Vote, Waveform EW, Wine EW). & To select significant features from datasets (Bioinformatics). \\ \hline P26 [87] & Synthetic and real trace datasets. & To solve task scheduling problems in cloud computing environments. \\ \hline P27 [88] & KNN & Wine, Dermatology, Heart, Ionosphere, Lung cancer, Thoracic surgery, Hepatitis, Parkinson, Phishing website, Qsar biodegradation, Absenteeism at work, Divorce, Wpdc, Risk factor cervical cancer, Wdpc. & For the FS task (Miscellaneous). \\ \hline P28 [89] & ANN & Digital Database for Screening Mammography (DDSM), Breast Cancer Wisconsin (WBC) dataset. & To solve the FS (Medical). \\ \hline P29 [90] & SVM & BreastCancer, KCL, WineEW, WDBC, Lung Cancer, Diabetic, Stock, Scene, Lymphography, and Parkinson. & For chemical descriptor selection and chemical compound activities (Chemical Engineering). \\ \hline P30 [91] & KNN & Exactly, Exactly 2, Lymphography, Spect EW, Congress EW, Ionosphere EW, Vote, Wine EW, Breast EW, Brain Tumors 1, Tumors 11, Leukemia 2, SRBCT, DLBCL, Prostate Tumors and Tumors 14. & To boost the FS process (Miscellaneous). \\ \hline P31 [92] & KNN & Breast Cancer, Breast EW, Congress EW, Exactly, Exactly 2, Heart EW, Ionosphere EW, Krvskp EW, Lymphography, M of N, Penglung EW, Sonar EW, Spect EW, Tic-tac-toe, Vote, Waveform EW, Wine EW and Zoo. \\ \hline P32 [93] & DT (48) & Heart-Cleveland, Dermatology, Hepatitis, Lung Cancer, Lymphography, Pima Indian Diabetes, Iris, Breast Cancer W, Diabetes, Heart-Stalog, Thyroid, Sonar, Gene. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Finally, the descriptions of the classifiers used by the aforementioned articles as wrappers are given in Table 5. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Classifier** & **Dataset** & **Application Domain** \\ \hline P33 [94] & J48 & Autos, Breast-w, Car, Glass, Heart-C, Dermatology, Hepatitis, Thoracic-Surgery, Lymph, Credit-g, Sonar, Ionosphere, Liver Disorders, Vote, Zoo. & For the FS tasks in classification (Miscellaneous). \\ \hline P34 [95] & SVM with RBF kernel & miniMIAS data with 100 mammograms (50-normal, 50-abnormal). & For FS in digital mammogram (Medical). \\ \hline P35 [17] & KNN & Lymphography, Diabetic, Cardiotocography, Cervical Cancer, Lung Cancer, Arrhythmia, Parkinson, Colon Tumor, Leukemia and three COVID-19 datasets. & To enhance FS for COVID-19 prediction (Medical). \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._