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A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection

Jungjit, Suwimol and Freitas, Alex A. (2015) A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 989-996. ISBN 978-1-4503-3488-4. (doi:10.1145/2739482.2768448) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

Abstract

This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based Feature Selection (GA-ML-CFS). This extension uses a LexGA as a global search method for generating candidate feature subsets. In our experiments, we compare the results obtained by LexGA-ML-CFS with the results obtained by the original hill climbing-based ML-CFS, the single-objective GA-ML-CFS and a baseline Binary Relevance method, using ML-kNN as the multi-label classifier. The results from our experiments show that LexGA-ML-CFS improved predictive accuracy, by comparison with other methods, in some cases, but in general there was no statistically significant different between the results of LexGA-ML-CFS and other methods.

Item Type: Book section
DOI/Identification number: 10.1145/2739482.2768448
Uncontrolled keywords: data mining, machine learning, multi-label classification, multi-objective optimization, evolutionary algorithms
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 12 Aug 2015 09:43 UTC
Last Modified: 23 Sep 2019 13:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50175 (The current URI for this page, for reference purposes)
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