An Improved Associative Classification Algorithm based on Incremental Rules

Mohamed Salem Almnnaee, Fadi Thabtah, Joan Lu

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

1 Citation (Scopus)

Abstract

In Associative classification (AC), the step of rule generation is necessarily exhaustive because of the inherited search problems from the association rule. Besides which, the entire rules set must be induced prior constructing the classifier. This article proposes a new AC algorithm called Dynamic Covering Associative Classification (DCAC) that learns each rule from a training dataset, removes its classified instances, and then learns the next rule from the remaining unclassified data rather than the original training dataset. This ensures that the exhaustive steps of rule evaluation and candidate generation will no longer be needed, thereby maintaining a real time rule generation process. The proposed algorithm constantly amends the support and confidence for each rule rather restricting itself with the support and confidence computed from the original dataset. Experiments on 20 datasets from different domains showed that the proposed algorithm generates higher quality and more accurate classifiers than other AC rule induction approaches.

Original languageEnglish
Title of host publicationProceedings of the 27th International Conference on Information Systems Development
Subtitle of host publicationDesigning Digitalization, ISD 2018
EditorsBo Andersson, Björn Johansson, S. Carlsson, Chris Barry, Michael Lang, Henry Linger, Christoph Schneider
PublisherAssociation for Information Systems
ISBN (Electronic)9789177538769
Publication statusPublished - 22 Aug 2018
Event27th International Conference on Information Systems Development: Designing Digitalization - Lund, Sweden
Duration: 22 Aug 201824 Aug 2018
Conference number: 27

Conference

Conference27th International Conference on Information Systems Development: Designing Digitalization
Abbreviated titleISD 2018
CountrySweden
CityLund
Period22/08/1824/08/18

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