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The granulation attribute reduction of multi-label data

The granulation attribute reduction of multi-label data In multi-label learning, the classification obtained by attribute set is usually inconsistent with that based on label set. This makes multi-label learning to possess fuzziness and uncertainty, which leads to the poor performance of learning algorithms. To address this fuzziness and uncertainty, this paper first combines multiple labels into a granulation multi-label decision function by using the classification obtained by attribute set. Then we give three kinds of formulations to construct the granulation multi-label decision functions, which are divided three levels, that is, macroscopic level, mesoscopic level and microscopic level. Moreover, by using the granulation multi-label decision function, this paper provides the method that transforms a multi-label decision table into a granulation multi-label decision table. Then three-level granulation attribute reductions of a multi-label decision table are defined and investigated. Furthermore, this paper shows that the existing multi-label attribute reduction, complementary decision reduction, can be viewed as the coarsest granularity reduction proposed by this paper, that is, a macroscopic-level granulation attribute reduction. In summary, this paper establishes a relatively systematic theoretical framework for attribute reduction of multi-label data based on rough set theory. Finally, by several comparative analysis, the reasonability, feasibility and effectiveness of these granulation attribute reductions are demonstrated. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

The granulation attribute reduction of multi-label data

Wang, Zhaohao; Zhang, Xiaoping
Applied Intelligence , Volume OnlineFirst – Mar 2, 2023
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19 pages

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-023-04510-x
Publisher site
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Abstract

In multi-label learning, the classification obtained by attribute set is usually inconsistent with that based on label set. This makes multi-label learning to possess fuzziness and uncertainty, which leads to the poor performance of learning algorithms. To address this fuzziness and uncertainty, this paper first combines multiple labels into a granulation multi-label decision function by using the classification obtained by attribute set. Then we give three kinds of formulations to construct the granulation multi-label decision functions, which are divided three levels, that is, macroscopic level, mesoscopic level and microscopic level. Moreover, by using the granulation multi-label decision function, this paper provides the method that transforms a multi-label decision table into a granulation multi-label decision table. Then three-level granulation attribute reductions of a multi-label decision table are defined and investigated. Furthermore, this paper shows that the existing multi-label attribute reduction, complementary decision reduction, can be viewed as the coarsest granularity reduction proposed by this paper, that is, a macroscopic-level granulation attribute reduction. In summary, this paper establishes a relatively systematic theoretical framework for attribute reduction of multi-label data based on rough set theory. Finally, by several comparative analysis, the reasonability, feasibility and effectiveness of these granulation attribute reductions are demonstrated.

Journal

Applied Intelligence – Springer Journals

Published: Mar 2, 2023

Keywords: Multi-label data; Rough set; Attribute reduction; Granulation attribute reduction; Complementary decision reduction

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