Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4978398 | SoftwareX | 2017 | 5 Pages |
Abstract
In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and cost functions for benchmarking experiments. We also provide illustrative examples, in which featsel outperforms the popular Weka workbench in feature selection procedures on data sets from the UCI Machine Learning Repository.
Related Topics
Physical Sciences and Engineering
Computer Science
Software
Authors
Marcelo S. Reis, Gustavo Estrela, Carlos Eduardo Ferreira, Junior Barrera,