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Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Multiclass Classification Through Multidimensional Clustering

Authors : Sara Silva, Luis Muñoz, Leonardo Trujillo, Vijay Ingalalli, Mauro Castelli, Leonardo Vanneschi

Published in: Genetic Programming Theory and Practice XIII

Publisher: Springer International Publishing

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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 multiclass 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.

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previous chapter Predicting Product Choice with Symbolic Regression and Classification
next chapter Prime-Time: Symbolic Regression Takes Its Place in the Real World
Footnotes
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Metadata
Title
Multiclass Classification Through Multidimensional Clustering
Authors
Sara Silva
Luis Muñoz
Leonardo Trujillo
Vijay Ingalalli
Mauro Castelli
Leonardo Vanneschi
Copyright Year
2016
Book
Genetic Programming Theory and Practice XIII

Print ISBN: 978-3-319-34221-4

Electronic ISBN: 978-3-319-34223-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-34223-8_13

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