Feature Selection: An Empirical Study
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Vandana C.P, Dr. Ajeet A. Chikkamannur
|DOI : 10.14445/22315381/IJETT-V69I2P223|
MLA Style: Vandana C.P, Dr. Ajeet A. Chikkamannur "Feature Selection: An Empirical Study" International Journal of Engineering Trends and Technology 69.2(2021):165-170.
APA Style:Vandana C.P, Dr. Ajeet A. Chikkamannur. Feature Selection: An Empirical Study. International Journal of Engineering Trends and Technology, 69(2), 165-170.
Feature Selection is inevitable in today’s decision-making system due to the enormous amount of heterogeneous, highly volatile data. It is important to choose the correct feature set to avoid Curse of Dimensionality and learn algorithms to behave effectively. If very few elements are chosen, satisfactory results may not be inferred, or if the number of features selected is very high, then performance is an issue. The accuracy can be improved by adding more relevant features. However, this is justifiable only up to a certain number of features. In this paper, we discussed the various types of feature selection techniques and carried out an empirical study.
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Feature Extraction, Entropy, Mutual Information, KNN, Clustering