TY - GEN
T1 - A multi-dimensional genetic programming approach for multi-class classification problems
AU - Ingalalli, Vijay
AU - Silva, Sara
AU - Castelli, Mauro
AU - Vanneschi, Leonardo
N1 - Ingalalli, V., Silva, S., Castelli, M., & Vanneschi, L. (2014). A multi-dimensional genetic programming approach for multi-class classification problems. In P. García-Sánchez, J. J. Merelo, V. M. Rivas Santos, M. Nicolau, K. Krawiec, M. I. Heywood, M. Castelli, ... K. Sim (Eds.), Genetic Programming - 17th European Conference, EuroGP 2014, Revised Selected Papers (pp. 48-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8599). Springer Verlag. https://doi.org/10.1007/978-3-662-44303-3_5
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84925089545&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-44303-3_5
DO - 10.1007/978-3-662-44303-3_5
M3 - Conference contribution
AN - SCOPUS:84925089545
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 48
EP - 60
BT - Genetic Programming - 17th European Conference, EuroGP 2014, Revised Selected Papers
A2 - García-Sánchez, Pablo
A2 - Merelo, Juan J.
A2 - Rivas Santos, Victor M.
A2 - Nicolau, Miguel
A2 - Krawiec, Krzysztof
A2 - Heywood, Malcolm I.
A2 - Castelli, Mauro
A2 - Sim, Kevin
PB - Springer Verlag
T2 - 17th European Conference on Genetic Programming, EuroGP 2014
Y2 - 23 April 2014 through 25 April 2014
ER -