Unsupervised Representative Feature Selection Algorithm Based on Information Entropy and Relevance Analysis

Unsupervised Representative Feature Selection Algorithm Based on Information Entropy and Relevance Analysis

Feature selection plays an important role in preprocessing in pattern recognition and data mining, especially in large scale image, digital text, and biological data. Specifically, class label information is unavailable for conducting the selection of a minimal feature subset in unsupervised learnin...

Full description

Bibliographic Details
Main Author: Yintong Wang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Feature selection
unsupervised learning
information entropy
relevance analysis
Markov blanket
Online Access:https://ieeexplore.ieee.org/document/8425982/

Internet

https://ieeexplore.ieee.org/document/8425982/

Similar Items