Stochastic and non-stochastic feature selection

Antonio J. Tallón-Ballesteros, Luís Correia, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings
EditorsHujun Yin, Minling Zhang, Yimin Wen, Guoyong Cai, Tianlong Gu, Antonio J. Tallon-Ballesteros, Junping Du, Yang Gao, Songcan Chen
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783319689340
Publication statusPublished - 2017
Event18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017 - Guilin, China
Duration: 2017 Oct 302017 Nov 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10585 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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