- author = "Luis Munoz and Sara Silva and Leonardo Trujillo",
- title = "{M3GP:} Multiclass Classification with {GP}",
- booktitle = "18th European Conference on Genetic Programming",
- year = "2015",
- editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
- series = "LNCS",
- volume = "9025",
- publisher = "Springer",
- pages = "78--91",
- address = "Copenhagen",
- month = "8-10 " # apr,
- organisation = "EvoStar",
- keywords = "genetic algorithms, genetic programming, M3GP, Classification, Multiple classes, Multidimensional clustering",
- isbn13 = "978-3-319-16500-4",
-
URL = "
https://novaresearch.unl.pt/en/publications/m3gp-multiclass-classification-with-gp",
-
URL = "https://rdcu.be/dakRS",
-
DOI = "
doi:10.1007/978-3-319-16501-1_7",
-
code_url = "https://github.com/jespb/Python-M3GP",
- size = "14 pages",
- abstract = "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 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 maximise 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.",
-
notes = "see perhaps https://github.com/jespb/Python-M3GP
Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015",
