Paper Info
Title
Feature selection based on cluster and variability analyses for ordinal multi-class classification problems
Abstract
Feature selection is an essential problem for pattern classification systems. This paper studies how to provide systems with the most characterizing features for ordinal multi-class classification task. The integration of cluster analyses and variability analyses advances a novel feature selection scheme with efficiency. The Huang-index method using fuzzy c-means is employed to enhance cluster validity and optimizes a consistent number of clusters among the features. A new entropy-based feature evaluation method is formulated for the authentication of relevant features. Then, multivariate statistical analyses are utilized to solve the redundancy between relevant features. Experimental results show that our new feature selection scheme sifts successfully a compact subset of characterizing features for classification problems with multiple classes.
Year
DOI
Venue
2013
10.1016/j.knosys.2012.07.018
Knowl.-Based Syst.
Keywords
Field
DocType
feature selection,variability analyses advance,relevant feature,ordinal multi-class classification task,ordinal multi-class classification problem,new entropy-based feature evaluation,huang-index method,classification problem,new feature selection scheme,pattern classification system,novel feature selection scheme,variability analysis,multivariate analysis,multi class classification
Data mining,Authentication,Feature selection,Computer science,Redundancy (engineering),Artificial intelligence,Multivariate analysis,Multiclass classification,Pattern recognition,Ordinal number,Multivariate statistics,Fuzzy logic,Machine learning
Journal
Volume
ISSN
Citations 
37,
0950-7051
11
PageRank 
References 
Authors
0.55
42
1
Name
Order
Citations
PageRank
Hung-Yi Lin1398.74