Class Strength Prediction Method for Associative Classification

Suzan Ayyat, Zhongyu Lu, Fadi Thabtah

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

Abstract

Test data prediction is about assigning the most suitable class for each test case during classification. In Associative Classification (AC) data mining, this step is considered crucial since the overall performance of the classifier is heavily dependent on the class assigned to each test case. This paper investigates the classification (prediction) step in AC in an attempt to come up with a novel generic prediction method that assures the best class assignment for each test case. The outcome is a new prediction method that takes into account all applicable rules ranking position in the classifier beside the class number of rules. Experimental results using different data sets from the University of California Irvine (UCI) repository and two common AC prediction methods reveal that the proposed method is more accurate for the majority of the data sets. Further, the proposed method can be plugged and used successfully by any AC algorithm.
Original languageEnglish
Title of host publicationIMMM 2014 The Fourth International Conference on Advances in Information Mining and Management
Place of PublicationParis, France
PublisherInternational Academy, Research, and Industry Association (IARIA)
Pages5-10
Number of pages6
ISBN (Electronic)9781612083643
Publication statusPublished - 20 Jul 2014
EventThe Fourth International Conference on Advances in Information Mining and Management - Paris, France
Duration: 20 Jul 201424 Jul 2014
Conference number: 4th
https://www.iaria.org/conferences2014/IMMM14.html (Link to Conference Website)

Conference

ConferenceThe Fourth International Conference on Advances in Information Mining and Management
Abbreviated titleIMMM 2014
CountryFrance
CityParis
Period20/07/1424/07/14
Internet address

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  • Cite this

    Ayyat, S., Lu, Z., & Thabtah, F. (2014). Class Strength Prediction Method for Associative Classification. In IMMM 2014 The Fourth International Conference on Advances in Information Mining and Management (pp. 5-10). Paris, France: International Academy, Research, and Industry Association (IARIA).