Paper Info
Title
Simultaneous Feature Selection and Extraction Using Fuzzy Rough Sets.
Abstract
In this chapter, a novel dimensionality reduction method, based on fuzzy rough sets, is presented, which simultaneously selects attributes and extracts features using the concept of feature significance. The method is based on maximizing both relevance and significance of the reduced feature set, whereby redundancy therein is removed. The chapter also presents classical and neighborhood rough sets for computing relevance and significance of the feature set and compares their performance with that of fuzzy rough sets based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness of the proposed fuzzy rough set-based dimensionality reduction method, along with a comparison with existing attribute selection and feature extraction methods, is demonstrated on real-life data sets.
Year
DOI
Venue
2012
10.1007/978-81-322-1602-5_13
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012)
Field
DocType
Volume
k-nearest neighbors algorithm,Decision tree,Dimensionality reduction,Feature selection,Pattern recognition,Computer science,Support vector machine,Rough set,Feature extraction,Redundancy (engineering),Artificial intelligence
Conference
236
ISSN
Citations 
PageRank 
2194-5357
1
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Pradipta Maji159648.40
Partha Garai2313.48