Selecting critical features for data classification based on machine learning methods

Selecting critical features for data classification based on machine learning methods

Abstract Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature...

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Bibliographic Details
Main Authors: Rung-Ching Chen, Christine Dewi, Su-Wen Huang, Rezzy Eko Caraka
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Journal of Big Data
Subjects:
Random Forest
Features selection
SVM
Classification
KNN
LDA
Online Access:http://link.springer.com/article/10.1186/s40537-020-00327-4

Internet

http://link.springer.com/article/10.1186/s40537-020-00327-4

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