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A grammatical evolution algorithm for the generation of hierarchical multi-label classification rules

Cerri, Ricardo and Barros, Rodrigo C. and de Carvalho, Andre C.P.L.F. and Freitas, Alex A. (2013) A grammatical evolution algorithm for the generation of hierarchical multi-label classification rules. In: 2013 IEEE Congress on Evolutionary Computation. IEEE, pp. 454-461. ISBN 978-1-4799-0453-2. E-ISBN 978-1-4799-0454-9. (doi:10.1109/CEC.2013.6557604) (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)

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Official URL
http://dx.doi.org/10.1109/CEC.2013.6557604

Abstract

Hierarchical Multi-Label Classification (HMC) is a challenging task in data mining and machine learning. Each instance in HMC can be classified into two or more classes simultaneously. These classes are structured in a hierarchy, in the form of either a tree or a directed acyclic graph. Therefore, an instance can be assigned to two or more paths from the hierarchical structure, resulting in a complex classification problem with hundreds or thousands of classes. Several methods have been proposed to deal with such problems, including several algorithms based on well-known bio-inspired techniques, such as neural networks, ant colony optimization, and genetic algorithms. In this work, we propose a novel global method called GEHM, which makes use of grammatical evolution for generating HMC rules. In this approach, the grammatical evolution algorithm evolves the antecedents of classification rules, in order to assign instances from a HMC dataset to a probabilistic class vector. Our method is compared to bio-inspired HMC algorithms in protein function prediction datasets. The empirical analysis conducted in this work shows that GEHM outperforms the bio-inspired algorithms with statistical significance, which suggests that grammatical evolution is a promising alternative to deal with hierarchical multi-label classification of biological data.

Item Type: Book section
DOI/Identification number: 10.1109/CEC.2013.6557604
Uncontrolled keywords: evolutinary algorithm, hierarchical classification, data mining, machine learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 01 Jul 2013 17:11 UTC
Last Modified: 17 Sep 2019 09:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34482 (The current URI for this page, for reference purposes)
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