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Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees Read chapter Read first chapter

2018 | OriginalPaper | Chapter

MetaUtil: Meta Learning for Utility Maximization in Regression

Authors : Paula Branco, Luís Torgo, Rita P. Ribeiro

Published in: Discovery Science

Publisher: Springer International Publishing

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Abstract

Several important real world problems of predictive analytics involve handling different costs of the predictions of the learned models. The research community has developed multiple techniques to deal with these tasks. The utility-based learning framework is a generalization of cost-sensitive tasks that takes into account both costs of errors and benefits of accurate predictions. This framework has important advantages such as allowing to represent more complex settings reflecting the domain knowledge in a more complete and precise way. Most existing work addresses classification tasks with only a few proposals tackling regression problems. In this paper we propose a new method, MetaUtil, for solving utility-based regression problems. The MetaUtil algorithm is versatile allowing the conversion of any out-of-the-box regression algorithm into a utility-based method. We show the advantage of our proposal in a large set of experiments on a diverse set of domains.

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Footnotes
1
Data set properties described in Sect. 5.
 
2
Further details available in [1].
 
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Metadata
Title
MetaUtil: Meta Learning for Utility Maximization in Regression
Authors
Paula Branco
Luís Torgo
Rita P. Ribeiro
Copyright Year
2018
Book
Discovery Science

Print ISBN: 978-3-030-01770-5

Electronic ISBN: 978-3-030-01771-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01771-2_9

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