A multi-dimensional genetic programming approach for multi-class classification problems

Vijay Ingalalli, Sara Silva, Mauro Castelli, Leonardo Vanneschi

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

25 Citations (Scopus)


Classification problems are of profound interest for the machine learning community as well as to an array of application fields. However, multi-class classification problems can be very complex, in particular when the number of classes is high. Although very successful in so many applications, GP was never regarded as a good method to perform multi-class classification. In this work, we present a novel algorithm for tree based GP, that incorporates some ideas on the representation of the solution space in higher dimensions. This idea lays some foundations on addressing multi-class classification problems using GP, which may lead to further research in this direction. We test the new approach on a large set of benchmark problems from several different sources, and observe its competitiveness against the most successful state-of-the-art classifiers.

Original languageEnglish
Title of host publicationGenetic Programming - 17th European Conference, EuroGP 2014, Revised Selected Papers
EditorsPablo García-Sánchez, Juan J. Merelo, Victor M. Rivas Santos, Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Kevin Sim
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)9783662443026
Publication statusPublished - 1 Jan 2014
Event17th European Conference on Genetic Programming, EuroGP 2014 - Granada, Spain
Duration: 23 Apr 201425 Apr 2014

Publication series

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


Conference17th European Conference on Genetic Programming, EuroGP 2014


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