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Literature Review on Feature Selection Methods for High-Dimensional Data

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
D. Asir Antony Gnana Singh, S. Appavu Alias Balamurugan, E. Jebamalar Leavline

Asir Antony Gnana D Singh, Appavu Alias S Balamurugan and Jebamalar E Leavline. Article: Literature Review on Feature Selection Methods for High-Dimensional Data. International Journal of Computer Applications 136(1):9-17, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {D. Asir Antony Gnana Singh and S. Appavu Alias Balamurugan and E. Jebamalar Leavline},
	title = {Article: Literature Review on Feature Selection Methods for High-Dimensional Data},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {1},
	pages = {9-17},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


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 high-dimensional 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 high-dimensional data.


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Introduction 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.