Feature Selection Techniques in Learning Algorithms to Predict Truthful Data

Abstract

Objectives: This review focuses on various feature selection process, strategy, and methods such as filter, wrapper and embedded algorithms and its advantages and disadvantages are presented. Methods: The algorithms such as Mutual Information Gain (MIG), Chi-Square (CS) and Recursive Feature Elimination (RFE) are used to select features. In this review, two benchmark datasets: Breast cancer and Diabetes are used. Findings: To improve the efficiency, selection of appropriate feature selection methods and algorithms are most important. To measure the performance of these selected features Random Forest model used as classifiers and compared with Support Vector Machine and Decision Tree models. Filter method and algorithm selects up to 15 features out of 17 for diabetes dataset with 89 % to 98 % of accuracy. For breast cancer dataset, up to 28 features out of 31 features selected with 98.5 % of accuracy. Wrapper method RFE selects 14 features from 17 for diabetes and 10 out of 31 features selected for breast cancer. This RFE method shows up to 98.25 % of accuracy for diabetes and 99.20% of accuracy for breast cancer. Novelty: Feature selection techniques help to improve the performance, efficiency and decrease the storage and processing time and build a better model for further process in prediction. The proper feature selection helps to diagnose diseases at an earlier stage and improve the survival of human beings.

A Hybrid Dimensionality Reduction for Network Intrusion Detection

“…In this study, we propose a hybrid dimensionality reduction module that harnesses the power of feature selection and feature extraction approaches to reduce the input feature space since unuseful features increase computational complexity and decrease accuracy. Usha and Anuradha [18] compared three feature selection techniques: mutual information gain, the chai-square method, and recursive feature elimination. They reported that recursive feature selection selects optimal features compared to other techniques.…”