A Fuzzy-Rough Sets Based Compact Rule Induction Method for Classifying Hybrid Data

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Yang Liu, Qinglei Zhou, Elisabeth Rakus-Andersson, Guohua Bai
Title: A Fuzzy-Rough Sets Based Compact Rule Induction Method for Classifying Hybrid Data
Translated title: A Fuzzy-Rough Sets Based Compact Rule Induction Method for Classifying Hybrid Data
Journal: Lecture Notes in Computer Science
Conference name: 7th International Conference on Rough Sets and Knowledge Technology
Year: 2012
Pagination: 63-70
ISBN: 978-3-642-31899-3
Publisher: Springer
City: Chengdu
URI/DOI: 10.1007/978-3-642-31900-6_9
Organization: Blekinge Institute of Technology
Department: School of Computing, School of Engineering - Dept. of Mathematics & Natural Sciences (Sektionen för datavetenskap och kommunikation, Sektionen för ingenjörsvetenskap - Avd.för matematik och naturvetenskap)
School of Computing S-371 79 Karlskrona, School of Engineering S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com; http://www.bth.se/ing/
Authors e-mail: liuyang2006@gmail.com
Language: English
Abstract: Rule induction plays an important role in knowledge discovery process. Rough set based rule induction algorithms are characterized by excellent accuracy, but they lack the abilities to deal with hybrid attributes such as numeric or fuzzy attributes. In real-world applications, data usually exists with hybrid formats, and thus a unified rule induction algorithm for hybrid data learning is desirable. We firstly model different types of attributes in equivalence relationship, and define the key concepts of block, minimal complex and local covering based on fuzzy rough sets model, then propose a rule induction algorithm for hybrid data learning. Furthermore, in order to estimate performance of the proposed method, we compare it with state-of-the-art methods for hybrid data learning. Comparative studies indicate that rule sets extracted by this method can not only achieve comparable accuracy, but also get more compact rule sets. It is therefore concluded that the proposed method is effective for hybrid data learning.
Subject: Computer Science\Artificial Intelligence
Computer Science\Computersystems
Computer Science\General
Keywords: Knowledge discovery, classification, rough sets, rule induction, hybrid data
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