A New Approach for Optimal Selection of Features for Classification Based on Rough Sets, Evolution and Neural Networks

Eddy Torres-Constante, Julio Ibarra-Fiallo, Monserrate Intriago-Pazmiño

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


In number recognition, one of the challenges is to deal with the high dimensionality of data that affects the performance of algorithms. On the other hand, pattern recognition allows establishing fundamental properties among sets of objects. In this context, Rough Set Theory applies the concept of super-reducts in order to find subsets of attributes that preserve the capability of the entire set to distinguish objects that belong to different classes. Nevertheless, finding these reducts for large data sets has exponential complexity due to the number of objects per class and attributes per object. This paper proposes a new approach for dealing with this complex problem in real data sets to obtain a close enough to a minimal discriminator. It takes advantage of the theoretical background of Rough Set Theory, especially considering those super-reducts of minimal length. In literature, there is an algorithm for finding these minimal length reducts. It performs well for a small sampling of objects per class of the entire data set. An evolutionary algorithm is performed to extend it over a huge data set, taking a subset of the entire list of super-reducts as the initial population. The proposed discriminator is evaluated and compared against state-of-the-art algorithms and data set declared performance for different models.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031160714
StatePublished - 31 Aug 2022
EventIntelligent Systems Conference, IntelliSys 2022 - Virtual, Online
Duration: 1 Sep 20222 Sep 2022

Publication series

NameLecture Notes in Networks and Systems
Volume542 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceIntelligent Systems Conference, IntelliSys 2022
CityVirtual, Online


  • Accuracy
  • Evolutionary strategy
  • Exponential complexity
  • Handwritten number classification
  • Minimal length
  • Neural networks
  • Pattern recognition
  • Super-reducts


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