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Erschienen in:
Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and “Do Not Care” Conditions

verfasst von : Patrick G. Clark, Cheng Gao, Jerzy W. Grzymala-Busse

Erschienen in: Rough Sets

Verlag: Springer International Publishing

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Abstract

In this paper we present results of novel experiments conducted on 12 data sets with many missing attribute values interpreted as attribute-concept values and “do not care” conditions. In our experiments complexity of rule sets, in terms of the number of rules and the total number of conditions induced from such data, are evaluated. The simpler rule sets are considered better. Our first objective was to check which interpretation of missing attribute values should be used to induce simpler rule sets. There is some evidence that the “do not care” conditions are better. Our secondary objective was to test which of the three probabilistic approximations: singleton, subset or concept, used for rule induction should be used to induce simpler rule sets. The best choice is the subset probabilistic approximation and the singleton probabilistic approximation is the worst choice.

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Vorheriges Kapitel Rough Set Approaches to Imprecise Modeling
Nächstes Kapitel Rough Sets Are -Accessible
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Metadaten
Titel
Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and “Do Not Care” Conditions
verfasst von
Patrick G. Clark
Cheng Gao
Jerzy W. Grzymala-Busse
Copyright-Jahr
2016
Buch
Rough Sets

Print ISBN: 978-3-319-47159-4

Electronic ISBN: 978-3-319-47160-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47160-0_6