Associative Text Categorisation Rules Pruning Method

Hussein Abu-Mansour, Wa'el Hadi, T. L. McCluskey, Fadi Thabtah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, the problem of rule pruning in associative text categorisation is investigated. We propose a new rule pruning method within an existing associative classification algorithm called MCAR. Experimental results against large text collection (Reuters-21578) using the developed pruning method as well as other known existing methods (Database coverage, lazy pruning) are conducted. The bases of the experiments are the classification accuracy and the number of generated rules. The results derived show that the proposed rule pruning method derives higher quality and more scalable classifiers than those produced by lazy and database coverage pruning approaches. In addition, the number of rules generated by the developed pruning procedure is usually less than those of lazy pruning and database coverage heuristics.

Original languageEnglish
Title of host publicationProceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention
EditorsRafal Rzepka
PublisherThe Society for the Study of Artifical Intelligence and the Simulation of Behaviour (SSAISB)
Pages39-44
Number of pages6
ISBN (Print)1902956885, 9781902956886
Publication statusPublished - 1 Dec 2010
Event1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents: A Symposium at the AISB 2010 Convention - De Montfort University, Leicester, United Kingdom
Duration: 29 Mar 20101 Apr 2010
Conference number: 1
https://www.scimagojr.com/journalsearch.php?q=21100204929&tip=sid&clean=0

Conference

Conference1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents
CountryUnited Kingdom
CityLeicester
Period29/03/101/04/10
Internet address

Fingerprint Dive into the research topics of 'Associative Text Categorisation Rules Pruning Method'. Together they form a unique fingerprint.

  • Cite this

    Abu-Mansour, H., Hadi, W., McCluskey, T. L., & Thabtah, F. (2010). Associative Text Categorisation Rules Pruning Method. In R. Rzepka (Ed.), Proceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention (pp. 39-44). The Society for the Study of Artifical Intelligence and the Simulation of Behaviour (SSAISB).