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2018 | OriginalPaper | Buchkapitel

8. Attribute Selection in a Dispersed Decision-Making System

verfasst von : Małgorzata Przybyła-Kasperek

Erschienen in: Advances in Feature Selection for Data and Pattern Recognition

Verlag: Springer International Publishing

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Abstract

In this chapter, the use of a method for attribute selection in a dispersed decision-making system is discussed. Dispersed knowledge is understood to be the knowledge that is stored in the form of several decision tables. Different methods for solving the problem of classification based on dispersed knowledge are considered. In the first method, a static structure of the system is used. In more advanced techniques, a dynamic structure is applied. Different types of dynamic structures are analyzed: a dynamic structure with disjoint clusters, a dynamic structure with inseparable clusters and a dynamic structure with negotiations. A method for attribute selection, which is based on the rough set theory, is used in all of the methods described here. The results obtained for five data sets from the UCI Repository are compared and some conclusions are drawn.

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Vorheriges Kapitel Ranking-Based Rule Classifier Optimisation

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Titel: Attribute Selection in a Dispersed Decision-Making System
verfasst von: Małgorzata Przybyła-Kasperek
Verlag: Springer International Publishing
Buch: Advances in Feature Selection for Data and Pattern Recognition
Print ISBN: 978-3-319-67587-9

Electronic ISBN: 978-3-319-67588-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-67588-6_8

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