Lazy classification algorithms based on deterministic and inhibitory dicision rules

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 same classification problem as in Chaps. 5 and 6: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. We compare two lazy [1] classification algorithms based on deterministic and inhibitory decision rules of the forms (a1(x) = b1) ∧...∧ (at(x) = bt) → d(x) = b, respectively, where a1,..., at are conditional attributes, b1,..., bt are values of these attributes, d is the decision attribute and b is a value of d. By Dec (T) we denote the set of values of the decision attribute d.

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
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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