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A new genetic algorithm for multi-label correlation-based feature selection.

Jungjit, Suwimol and Freitas, Alex A. (2015) A new genetic algorithm for multi-label correlation-based feature selection. In: ESANN 2015 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, pp. 285-290. ISBN 978-2-87587-014-8.

Abstract

This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi-label classification algorithm - in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS), across 10 multi-label datasets.

Item Type: Book section
Uncontrolled keywords: multi-label classification, data mining, machine learning, evolutionary algorithms, feature selection
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 15 May 2015 12:55 UTC
Last Modified: 14 Oct 2019 08:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48529 (The current URI for this page, for reference purposes)
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