A Two-Phase Approach for Semi-Supervised Feature Selection

A Two-Phase Approach for Semi-Supervised Feature Selection

This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is divided into two parts: The first part, which contain...

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Bibliographic Details
Main Authors: Amit Saxena, Shreya Pare, Mahendra Singh Meena, Deepak Gupta, Akshansh Gupta, Imran Razzak, Chin-Teng Lin, Mukesh Prasad
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Algorithms
Subjects:
feature selection
semi-supervised datasets
classification
clustering
correlation
Online Access:https://www.mdpi.com/1999-4893/13/9/215

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https://www.mdpi.com/1999-4893/13/9/215

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