Skip to main navigation Skip to search Skip to main content

Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm

  • Institute of Innovative Technologies EMAG

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

17 Citations (Scopus)

Abstract

The proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. The analysis has been done on twenty one benchmark data sets.

Original languageEnglish
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing - 13th International Conference, RSFDGrC 2011, Proceedings
Pages278-285
Number of pages8
DOIs
Publication statusPublished - 2011
Event13th International Conference on Rough Sets, Fuzzy Sets and Granular Computing, RSFDGrC 2011 - Moscow, Russian Federation
Duration: 25 Jun 201127 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6743 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Rough Sets, Fuzzy Sets and Granular Computing, RSFDGrC 2011
Country/TerritoryRussian Federation
CityMoscow
Period25/06/1127/06/11

Keywords

  • classification
  • rules induction
  • rules quality measures

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm'. Together they form a unique fingerprint.

Cite this