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A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains

Goncalves, Eduardo Corrêa and Plastino, Alexandre and Freitas, Alex A. (2013) A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE, pp. 469-476. ISBN 978-1-4799-2971-9. E-ISBN 978-1-4799-2972-6. (doi:10.1109/ICTAI.2013.76) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICTAI.2013.76

Abstract

First proposed in 2009, the classifier chains model (CC) has become one of the most influential algorithms for multi-label classification. It is distinguished by its simple and effective approach to exploit label dependencies. The CC method involves the training of q single-label binary classifiers, where each one is solely responsible for classifying a specific label in l1,..., lq. These q classifiers are linked in a chain, such that each binary classifier is able to consider the labels predicted by the previous ones as additional information at classification time. The label ordering has a strong effect on predictive accuracy, however it is decided at random and/or combining random orders via an ensemble. A disadvantage of the ensemble approach consists of the fact that it is not suitable when the goal is to generate interpretable classifiers. To tackle this problem, in this work we propose a genetic algorithm for optimizing the label ordering in classifier chains. Experiments on diverse benchmark datasets, followed by the Wilcoxon test for assessing statistical significance, indicate that the proposed strategy produces more accurate classifiers.

Item Type: Book section
DOI/Identification number: 10.1109/ICTAI.2013.76
Uncontrolled keywords: data mining, machine learning, evolutionary algorithms, classification
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 21 Jan 2014 14:30 UTC
Last Modified: 25 Sep 2019 11:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37822 (The current URI for this page, for reference purposes)
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