- author = "Sara Silva and Luis Munoz and Leonardo Trujillo and Vijay Ingalalli and Mauro Castelli and Leonardo Vanneschi",
- title = "Multiclass Classification Through Multidimensinoal Clustering",
- booktitle = "Genetic Programming Theory and Practice XIII",
- year = "2015",
- editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon",
- series = "Genetic and Evolutionary Computation",
- pages = "219--239",
- address = "Ann Arbor, USA",
- month = "14-16 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Classification, Multiple classes, Clustering",
- isbn13 = "978-3-319-34223-8",
-
URL = "
http://www.springer.com/us/book/9783319342214",
-
DOI = "
doi:10.1007/978-3-319-34223-8_13",
- abstract = "Classification is one of the most important machine learning tasks in science and engineering. However, it can be a difficult task, in particular when a high number of classes is involved. Genetic Programming, despite its recognized successfulness in so many different domains, is one of the machine learning methods that typically struggles, and often fails, to provide accurate solutions for multi-class classification problems. We present a novel algorithm for tree based GP that incorporates some ideas on the representation of the solution space in higher dimensions, and can be generalized to other types of GP. We test three variants of this new approach on a large set of benchmark problems from several different sources, and observe their competitiveness against the most successful state-of-the-art classifiers like Random Forests, Random Subspaces and Multilayer Perceptron.",
-
notes = "
Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016",
