Majority Classification by Means of Association Rules

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Item Type: Proceeding
MIUR type: Proceedings > Proceedings
Title: Majority Classification by Means of Association Rules
Authors string: Baralis E., Garza P.
University authors:
Page Range: pp. 35-46
Journal or Publication Title: LECTURE NOTES IN COMPUTER SCIENCE
Referee type: Not specified type
Publisher: Springer Berlin Heidelberg
ISSN: 0302-9743
Volume: 2838
Event Title: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases
Event Location: Cavtat-Dubrovnik; Croatia
Event relevance: International
Abstract: Associative classification is a well-known technique for structured data classification. Most previous work on associative classification based the assignment of the class label on a single classification rule. In this work we propose the assignment of the class label based on simple majority voting among a group of rules matching the test case. We propose a new algorithm, L3M, which is based on previously proposed algorithm L3. L3 performed a reduced amount of pruning, coupled with a two step classification process. L3M combines this approach with the use of multiple rules for data classification. The use of multiple rules, both during database coverage and classification, yields an improved accuracy
Date: 2003
Status: Published
Language of publication: English
Uncontrolled Keywords:
Departments (original): DAUIN - Control and Computer Engineering
Departments: DAUIN - Department of Control and Computer Engineering
Related URLs:
Subjects: Area 09 - Ingegneria industriale e dell'informazione > SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Date Deposited: 15 Feb 2007 17:02
Last Modified: 24 Oct 2014 12:15
Id Number (DOI): 10.1007/978-3-540-39804-2_6
Permalink: http://porto.polito.it/id/eprint/1510808
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