TY - GEN
T1 - M3GP – multiclass classification with GP
AU - Muñoz, Luis
AU - Trujillo, Leonardo
AU - Silva, Sara
PY - 2015
Y1 - 2015
N2 - Data classification is one of the most ubiquitous machine learning tasks in science and engineering. However, Genetic Programming is still not a popular classification methodology, partially due to its poor performance in multiclass problems. The recently proposed M2GP - Multidimensional Multiclass Genetic Programming algorithm achieved promising results in this area, by evolving mappings of the p-dimensional data into a d-dimensional space, and applying a minimum Mahalanobis distance classifier. Despite good performance, M2GP employs a greedy strategy to set the number of dimensions d for the transformed data, and fixes it at the start of the search, an approach that is prone to locally optimal solutions. This work presents the M3GP algorithm, that stands for M2GP with multidimensional populations. M3GP extends M2GP by allowing the search process to progressively search for the optimal number of new dimensions d that maximize the classification accuracy. Experimental results show that M3GP can automatically determine a good value for d depending on the problem, and achieves excellent performance when compared to state-of-the-art-methods like Random Forests, Random Subspaces and Multilayer Perceptron on several benchmark and real-world problems.
AB - Data classification is one of the most ubiquitous machine learning tasks in science and engineering. However, Genetic Programming is still not a popular classification methodology, partially due to its poor performance in multiclass problems. The recently proposed M2GP - Multidimensional Multiclass Genetic Programming algorithm achieved promising results in this area, by evolving mappings of the p-dimensional data into a d-dimensional space, and applying a minimum Mahalanobis distance classifier. Despite good performance, M2GP employs a greedy strategy to set the number of dimensions d for the transformed data, and fixes it at the start of the search, an approach that is prone to locally optimal solutions. This work presents the M3GP algorithm, that stands for M2GP with multidimensional populations. M3GP extends M2GP by allowing the search process to progressively search for the optimal number of new dimensions d that maximize the classification accuracy. Experimental results show that M3GP can automatically determine a good value for d depending on the problem, and achieves excellent performance when compared to state-of-the-art-methods like Random Forests, Random Subspaces and Multilayer Perceptron on several benchmark and real-world problems.
KW - Classification
KW - Genetic programming
KW - Multidimensional clustering
KW - Multiple classes
UR - http://www.scopus.com/inward/record.url?scp=84925070921&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16501-1_7
DO - 10.1007/978-3-319-16501-1_7
M3 - Conference contribution
AN - SCOPUS:84925070921
VL - 9025
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 91
BT - Genetic Programming - 18th European Conference, EuroGP 2015, Proceedings
PB - Springer-Verlag
T2 - 18th European Conference on Genetic Programming, EuroGP 2015
Y2 - 8 April 2015 through 10 April 2015
ER -