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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2019

30.01.2018 | Original Article

Roughness measure based on description ability for attribute reduction in information system

verfasst von: Fachao Li, Chenxia Jin, Jinning Yang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2019

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Abstract

As a quantitative index of processing uncertain information by rough set theory, roughness measure is the basis of many decision-making problems such as resource management, system optimization etc. Therefore constructing roughness measure reflecting different decision preference has important theoretical and practical value. In this paper, we first analyze the characteristics and deficiencies of Pawlak roughness, and further propose the concepts of lower (upper) accuracy. We second establish an description ability-based roughness measure (DRD) by combining with two basic measure factors-lower (upper) accuracy. We third analyze the characteristics of DRD and further give some sufficient and necessary conditions. Finally, we propose a DRD-based reduction method (DRD-RM), and discuss the difference and relation between DRD-RM and the existing reduction methods by experimental analysis for UCI data. The experimental results show that DRD-RM is an effective technique.
Vorheriger Artikel Feature selection based on generalized variable-precision -fuzzy granular rough set model over two universes
Nächster Artikel A comprehensive study on color correction for medical facial images

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Metadaten
Titel
Roughness measure based on description ability for attribute reduction in information system
verfasst von
Fachao Li
Chenxia Jin
Jinning Yang
Publikationsdatum
30.01.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0771-8

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