Paper Info
Title
Reduced gene subset selection based on discrimination power boosting for molecular classification.
Abstract
Traditional feature selection methods have two major inappropriate designs in their criterion. Firstly, they trade the profit of relevant information off against the risk of redundant information. Secondly, they cannot get rid of the well-known trap that “the m best features are not the best m features”. There is no necessary inheritance between two consecutive selection rounds. As a remedy for the first problem, we propose a new selection criterion, which concentrates on verifying discrimination boosting effect. A novel feature selection scheme is also proposed in this paper as a mend on the second problem and it can generate multiple subsets with variable feature combinations supporting classification tasks. Our experimental results show that different subsets composed of variable selected features can have so quite similar discrimination power that they might achieve resembled classification quality. These experimental results also verify that our proposed method can successfully explore simple reduced subsets of genes for several genetic datasets with both efficacy and efficiency.
Year
DOI
Venue
2018
10.1016/j.knosys.2017.11.036
Knowledge-Based Systems
Keywords
Field
DocType
Discrimination power,Feature selection,Molecular classification,Information gain,Cluster analyses
Feature selection,Computer science,Information gain,Selection criterion,Boosting (machine learning),Artificial intelligence,Machine learning
Journal
Volume
ISSN
Citations 
142
0950-7051
2
PageRank 
References 
Authors
0.36
25
1
Name
Order
Citations
PageRank
Hung-Yi Lin1398.74