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Authors: Illya Bakurov 1 ; Mauro Castelli 1 ; Francesco Fontanella 2 and Leonardo Vanneschi 1

Affiliations: 1 NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon and Portugal ; 2 Department of Electrical Engineering and Information, Universitá degli Studi di Cassino e del Lazio Meridionale, 03043, Cassino and Italy

ISBN: 978-989-758-384-1

ISSN: 2184-2825

Keyword(s): Classification, Regression, Geometric Semantic Genetic Programming.

Abstract: Geometric Semantic Genetic Programming (GSGP) is a recent variant of Genetic Programming, that is gaining popularity thanks to its ability to induce a unimodal error surface for any supervised learning problem. Nevertheless, so far GSGP has been applied to the real world basically only on regression problems. This paper represents an attempt to apply GSGP to real world classification problems. Taking inspiration from Perceptron neural networks, we represent class labels as numbers and we use an activation function to constraint the output of the solutions in a given range of possible values. In this way, the classification problem is turned into a regression one, and traditional GSGP can be used. In this work, we focus on binary classification; logistic constraining outputs in [0,1] is used as an activation function and the class labels are transformed into 0 and 1. The use of the logistic activation function helps to improve the generalization ability of the system. The presented res ults are encouraging: our regression-based classification system was able to obtain results that are better than, or comparable to, the ones of a set of competitor machine learning methods, on a rather rich set of real-life test problems. (More)

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Paper citation in several formats:
Bakurov, I.; Castelli, M.; Fontanella, F. and Vanneschi, L. (2019). A Regression-like Classification System for Geometric Semantic Genetic Programming. In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019) ISBN 978-989-758-384-1 ISSN 2184-2825, pages 40-48. DOI: 10.5220/0008052900400048

@conference{ecta19,
author={Illya Bakurov. and Mauro Castelli. and Francesco Fontanella. and Leonardo Vanneschi.},
title={A Regression-like Classification System for Geometric Semantic Genetic Programming},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019)},
year={2019},
pages={40-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008052900400048},
isbn={978-989-758-384-1},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019)
TI - A Regression-like Classification System for Geometric Semantic Genetic Programming
SN - 978-989-758-384-1
IS - 2184-2825
AU - Bakurov, I.
AU - Castelli, M.
AU - Fontanella, F.
AU - Vanneschi, L.
PY - 2019
SP - 40
EP - 48
DO - 10.5220/0008052900400048

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