featsel: A framework for benchmarking of feature selection algorithms and cost functions

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...

Full description

Saved in:
Bibliographic Details
Published in:SoftwareX 2017, Vol.6, p.193-197
Main Authors: Reis, Marcelo S., Estrela, Gustavo, Ferreira, Carlos Eduardo, Barrera, Junior
Format: Article
Language:English
Subjects:
Benchmarking
Boolean lattice
Combinatorial optimization
Feature selection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2017.07.005