Partial covers and inhibitory decision rules with weights

Pawel Delimata*, Mikhail Moshkov, Andrzej Skowron, Zbigniew Suraj

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

In this chapter, we consider the case, where each subset, used for covering, has its own weight, and we should minimize the total weight of subsets in partial cover. The same situation is with partial inhibitory decision rules: each conditional attribute has its own weight, and we should minimize the total weight of attributes occurring in partial inhibitory decision rule. If weights of attributes characterize time complexity of attribute value computation, then we try to minimize total time complexity of computation of attributes from partial inhibitory decision rule. If weights characterize a risk of attribute value computation (as in medical or technical diagnosis), then we try to minimize total risk, etc.

Original languageEnglish (US)
Title of host publicationInhibitory Rules in Data Analysis
Subtitle of host publicationA Rough Set Approach
EditorsPawel Delimata, Zbigniew Suraj, Mikhail Moshkov, Andrzej Skowron
Pages63-79
Number of pages17
DOIs
StatePublished - Jan 1 2009

Publication series

NameStudies in Computational Intelligence
Volume163
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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