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A comparison study of optimal scale combination selection in generalized multi-scale decision tables

Zeitschrift:: International Journal of Machine Learning and Cybernetics

Autoren:: Wei-Zhi Wu, Yee Leung

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Abstract

Traditional rough set approach is mainly used to unravel rules from a decision table in which objects can possess a unique attribute-value. In a real world data set, for the same attribute objects are usually measured at different scales. The main objective of this paper is to study optimal scale combinations in generalized multi-scale decision tables. A generalized multi-scale information table is an attribute-value system in which different attributes are measured at different levels of scales. With the aim of investigating knowledge representation and knowledge acquisition in inconsistent generalized multi-scale decision tables, we first introduce the notion of scale combinations in a generalized multi-scale information table. We then formulate information granules with different scale combinations in multi-scale information systems and discuss their relationships. Furthermore, we define lower and upper approximations of sets with different scale combinations and examine their properties. Finally, we examine optimal scale combinations in inconsistent generalized multi-scale decision tables. We clarify relationships among different concepts of optimal scale combinations in inconsistent generalized multi-scale decision tables.

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Titel: A comparison study of optimal scale combination selection in generalized multi-scale decision tables
Autoren:: Wei-Zhi Wu
Yee Leung
Publikationsdatum: 04.05.2019
DOI: https://doi.org/10.1007/s13042-019-00954-1
Verlag: Springer Berlin Heidelberg
Zeitschrift: International Journal of Machine Learning and Cybernetics
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X

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