Paper Info
Title
Consistency measures for feature selection: a formal definition, relative sensitivity comparison and a fast algorithm
Abstract
Consistency-based feature selection is an important category of feature selection research yet is defined only intuitively in the literature. First, we formally define a consistency measure, and then using this definition, evaluate 19 feature selection measures from the literature. While only 5 of these were labeled as consistency measures by their original authors, by our definition, an additional 9 measures should be classified as consistency measures. To compare these 14 consistency measures in terms of sensitivity, we introduce the concept of quasi-linear compatibility order, and partially determine the order among the measures. Next, we propose a new fast algorithm for consistency-based feature selection. We ran experiments using eleven large datasets to compare the performance of our algorithm against INTERACT and LCC, the only two instances of consistency-based algorithms with potential real world application. Our algorithm shows vast improvement in time efficiency, while its performance in accuracy is comparable with that of INTERACT and LCC.
Year
DOI
Venue
2011
10.5591/978-1-57735-516-8/IJCAI11-251
IJCAI
Keywords
DocType
Citations 
relative sensitivity comparison,consistency measure,new fast algorithm,consistency-based feature selection,important category,feature selection measure,feature selection research,formal definition,original author,quasi-linear compatibility order,consistency-based algorithm,eleven large datasets
Conference
11
PageRank 
References 
Authors
0.66
8
3
Name
Order
Citations
PageRank
Kilho Shin18910.44
Danny Fernandes2110.66
Seiya Miyazaki3354.58