NDLI: Towards better generalization in Pittsburgh learning classifier systems

Content Provider	IEEE Xplore Digital Library
Author	Santu, S.K.K. Rahman, M.M. Islam, M.M. Murase, K.
Copyright Year	2014
Description	Author affiliation: Dept. of Human & Artificial Intell. Syst., Univ. of Fukui, Fukui, Japan (Murase, K.) \|\| Dept. of Comput. Sci. & Eng., Bangladesh Univ. of Eng. & Technol. (BUET), Dhaka, Bangladesh (Santu, S.K.K.; Rahman, M.M.; Islam, M.M.)
Abstract	Generalization ability of a classifier is an important issue for any classification task. This paper proposes a new evolutionary system, i.e., EDARIC, based on the Pittsburgh approach for evolutionary machine learning and classification. The new system uses a destructive approach that starts with large-sized rules and gradually decreases the sizes as evolution progresses. Unlike most previous works, EDARIC adopts an intelligent deletion mechanism, evolves a separate population for each class of a given problem and uses an ensemble system to classify unknown instances. These features help in avoiding over-fitting and class-imbalance problems, which are beneficial for improving generalization ability of a classification system. EDARIC also applies a rule post-processing step to exempt the evolution phase from the burden of tuning a large number of parameters. Experimental results on various benchmark classification problems reveal that EDARIC has better generalization ability in case of both standard and imbalanced datasets compared to many existing algorithms in the literature.
Starting Page	1666
Ending Page	1673
File Size	528865
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900388
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Sociology Statistics Wheels Standards Training Biological cells
Content Type	Text
Resource Type	Article

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