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Exploring attribute selection in hierarchical classification.

Paes, B.C., Plastino, A., Freitas, Alex A. (2014) Exploring attribute selection in hierarchical classification. Journal of Information and Data Management, 5 (1). pp. 124-133. ISSN 2178-7107. (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)

Abstract

In the domain of many classification problems, classes have relations of dependency that are represented in hierarchical structures. These problems are known as hierarchical classification problems. Methods based on different approaches, considering hierarchical relations in different ways, have been proposed to solve them, in the attempt to achieve better predictive performance. In this work, we explore attribute selection techniques in conjunction with hierarchical classifiers from different categories, with the goal of improving their respective performances. Computational experiments, made with 18 hierarchical datasets, have indicated that the adopted classifiers attain better predictive accuracy when the most relevant attributes are considered in their construction.

Item Type: Article
Uncontrolled keywords: hierarchical classification, data mining, machine learning, lazy attribute selection, lazy learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 31 Jul 2014 15:36 UTC
Last Modified: 29 May 2019 12:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/42048 (The current URI for this page, for reference purposes)
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