NDLI: CompactLEM2: A scalable rough set based knowledge acquisition method that generates small number of short rules

Content Provider	IEEE Xplore Digital Library
Author	Yang Liu Guohua Bai Boqin Feng
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Xi'an Jiaotong Univ., Xi'an (Yang Liu)
Abstract	The complexity of knowledge plays an important role in the success of any types of knowledge acquisition algorithms performing on large-scale database. LERS (learning from examples based on rough sets) system is a rule based knowledge acquisition system that is characterized by excellent accuracy, but the complexity of generated rule set is not taken into account. This may cause interpretation problems for human and the classification knowledge may over fit training data. In this paper, CompactLEM2 is proposed as a scalable knowledge acquisition method that extracts rule set with easily understood rule forms, i.e., small size of rule set and short rule forms, without sacrificing classification accuracy. The main advantage of CompactLEM2 is its high efficiency. It can also produce compact rule set that fully or approximately describes classifications of given examples. We theoretically and experimentally show that CompactLEM2 exhibits log-linear asymptotic complexity with the number of training examples in most cases. We also present an example to illustrate characteristics of this algorithm. Finally, the capabilities of our method are demonstrated on eleven datasets. Experimental results are encouraging, and show that the length of extracted rule forms are short, and size of rule set is small, keeping the same level of classification accuracy of other rule acquisition methods in LERS system.
Starting Page	215
Ending Page	222
File Size	691176
Page Count	8
File Format	PDF
ISBN	9781424425389
DOI	10.1109/COGINF.2008.4639171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	classification Knowledge acquisition rough set LERS data mining system rule induction
Content Type	Text
Resource Type	Article

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