Feature selection plays a significant role in improving the performance of the machine learning algorithms in terms of reducing the time to build the learning model and increasing the accuracy in the learning process. Therefore, the researchers pay more attention on the feature selection to enhance the performance of the machine learning algorithms. Identifying the suitable feature selection method is very essential for a given machine learning task with highdimensional data. Hence, it is required to conduct the study on the various feature selection methods for the research community especially dedicated to develop the suitable feature selection method for enhancing the performance of the machine learning tasks on high-dimensional data. In order to fulfill this objective, this paper devotes the complete literature review on the various feature selection methods for highdimensional data.
General TermsLiterature review on feature selection methods, study on feature selection, wrapper-based feature selection, embeddedbased feature selection, hybrid feature selection, filter-based feature selection, feature subset-based feature selection, feature ranking-based feature selection, attribute selection, dimensionality reduction, variable selection, survey on feature selection, feature selection for high-dimensional data, introduction to variable and feature selection, feature selection for classification.
KeywordsIntroduction to variable and feature selection, information gain-based feature selection, gain ratio-based feature selection, symmetric uncertainty-based feature selection, subset-based feature selection, ranking-based feature selection, wrapper-based feature selection, embedded-based feature selection, filter-based feature selection, hybrid feature selection, selecting feature from high-dimensional data.
