Rough Sets Based Inequality Rule Learner for Knowledge Discovery

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Yang Liu, Guohua Bai, Qinglei Zhou, Elisabeth Rakus-Andersson
Title: Rough Sets Based Inequality Rule Learner for Knowledge Discovery
Translated title: Rough Sets Based Inequality Rule Learner for Knowledge Discovery
Conference name: 8th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2012
Year: 2012
Pagination: 100-105
ISBN: 978-3-642-32114-6
Publisher: Springer
City: Chengdu
URI/DOI: 10.1007/978-3-642-32115-3_11
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Mathematics & Natural Sciences (Sektionen för ingenjörsvetenskap - Avd.för matematik och naturvetenskap)
School of Engineering S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/ing/
Authors e-mail: liuyang2006@gmail.com
Language: English
Abstract: Traditional rule learners employ equality relations between attributes and values to express decision rules. However, inequality relationships, as supplementary relations to equation, can make up a new function for complex knowledge acquisition. We firstly discuss an extended compensatory model of decision table, and examine how it can simultaneously express both equality and inequality relationships of attributes and values. In order to cope with large-scale compensatory decision table, we propose a scalable inequality rule leaner, which initially compresses the input spaces of attribute value pairs. Example and experimental results show that the proposed learner can generate compact rule sets that maintain higher classification accuracies than equality rule learners.
Subject: Computer Science\Artificial Intelligence
Computer Science\General
Computer Science\Computersystems
Keywords: Classification, machine learning, rough sets, rule induction, inequality rule.
Note: Lecture Notes in Computer Science, 2012, Volume 7413/2012, 100-105,
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