Open Access
ARTICLE
Feature Selection Based on Distance Measurement
Mingming Yang*, Junchuan Yang
School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Mingming Yang. Email:
Journal of New Media 2021, 3(1), 19-27. https://doi.org/10.32604/jnm.2021.018267
Received 03 March 2021; Accepted 05 March 2021; Issue published 15 March 2021
Abstract
Every day we receive a large amount of information through different
social media and software, and this data and information can be realized with the
advent of data mining methods. In the process of data mining, to solve some
high-dimensional problems, feature selection is carried out in limited training
samples, and effective features are selected. This paper focuses on two Relief
feature selection algorithms: Relief and ReliefF algorithm. The differences
between them and their respective applicable scopes are analyzed. Based on
Relief algorithm, the high weight feature subset is obtained, and the correlation
between features is calculated according to the mutual information distance
measure, and the high redundant features are removed to obtain the feature
subset with higher quality. Experimental results on six datasets show the
effectiveness of our method.
Keywords
Cite This Article
M. Yang and J. Yang, "Feature selection based on distance measurement,"
Journal of New Media, vol. 3, no.1, pp. 19–27, 2021. https://doi.org/10.32604/jnm.2021.018267
Citations