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Towards a method for automatically selecting and configuring multi-label classification algorithms

de Sá, Alex G.C. and Pappa, Gisele L. and Freitas, Alex A. (2017) Towards a method for automatically selecting and configuring multi-label classification algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 1125-1132. ISBN 978-1-4503-4939-0. (doi:10.1145/3067695.3082053) (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/10.1145/3067695.3082053

Abstract

Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automatic ML (Auto-ML) area has emerged to solve this task. With this issue in mind, in this work we are interested in a specific type of classification problem, called multi-label classification (MLC). In MLC, each example in the dataset can be associated to one or more class labels, making the task considerably harder than traditional, single-label classification. In addition, the cost of learning raises due to the higher complexity of the data. Although the literature has proposed some methods to solve the Auto-ML task, those methods address only the traditional, single-label classification problem. By contrast, this work proposes the first method (an evolutionary algorithm) for solving the Auto-ML task in MLC, i.e., the first method for automatically selecting and configuring the best MLC algorithm for a given input dataset. The proposed evolutionary algorithm is evaluated on three MLC datasets, and compared against two baseline methods according to four different multi-label predictive accuracy measures. The results show that the proposed evolutionary algorithm is competitive against the baselines, but there is still room for improvement.

Item Type: Book section
DOI/Identification number: 10.1145/3067695.3082053
Uncontrolled keywords: automated machine learning, data mining, evolutionary algorithms, multi-label 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: 18 Aug 2017 09:51 UTC
Last Modified: 24 Sep 2019 08:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62887 (The current URI for this page, for reference purposes)
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