Two families of classification algorithms

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

In the paper, two families of lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on ordinary and inhibitory rules, but the direct generation of rules is not required. Instead of this, the considered algorithms extract efficiently for a new object some information on the set of rules which is next used by a decision-making procedure.

Original languageEnglish (US)
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings
Pages297-304
Number of pages8
StatePublished - Dec 1 2007
Event11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007 - Toronto, Canada
Duration: May 14 2007May 17 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4482 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007
CountryCanada
CityToronto
Period05/14/0705/17/07

Keywords

  • Decision tables
  • Information systems
  • Rough sets
  • Rules

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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