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Subject: search.filters.subject.Learning classifier system

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    Cost-sensitive XCS classifier system addressing imbalance problems
    (2008-12-01)
    Thach, Nguyen Huy
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    Rojanavasu, Porntep
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    Pinngern, Ouen
    The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor generalization for the minority class instances. This paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and cost-sensitive learning. In our approach, the reward value of correctly identifying the positive (rare) class outweighs the value of correctly identifying the common class. This research provides guidelines to set reward base on the dataset imbalance ratio and a method to calculate reward online base on the information collected by XCS during training is also proposed. Experimental results on synthetic and real-life datasets show that, with appropriate reward settings, XCS is robust to class imbalances. © 2008 IEEE.
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    Towards adapting XCS for imbalance problems
    (2008-12-01)
    Nguyen, Thach Huy
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    Foitong, Sombut
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    Srinil, Phaitoon
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    Pinngern, Ouen
    The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor performance in classifying the minority class instances. This paper analyzes the imbalance problem in accuracy-based learning classifier system XCS. XCS has shown excellent performance on some data mining tasks, but as other classifiers, it also performs poorly on imbalance data problems. We analyze XCS's behavior on various imbalance levels and propose an appropriate parameter tuning to improve performance of the system. Particularly, XCS is adapted to eliminate over-general classifiers and protect accurate classifiers of minority class. Experimental results in Boolean function problems show that, with proposal adaptations, XCS is robust to class imbalance. © 2008 Springer Berlin Heidelberg.
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    Rough set and XCS in classification problems
    (2008-09-22)
    Nguyen, Thach H.
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    Foitong, Sombut
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    Pinngern, Ouen
    XCS is known to degrade in classification performance when faced with many features that are redundant for rules discovery. In this paper, we propose a novel system combining of rough sets and XCS to deal with the mentioned problem. Firstly, rough set theory is used to handle inconsistent input datasets. The purpose of feature reduction by rough set is to identify the most significant attributes and eliminate the irrelevant ones to form a good feature subset for classification. Secondly, the reduced datasets are used to create a set of rules by using XCS. The main contribution of XCS to learning theory is its rules generation without experts. Finally, by applying the set of rules, we can classify unseen datasets into their specific classes. Experimental results on real-life datasets show that the proposed method can reduce storage space as well as can preserve and may also improve solution accuracy. Beside that, the rule retrieval time is also greatly reduced because the use of Rough-XCS classifier contains a smaller amount of instances with fewer features. Furthermore, the proposed method has a high potential to be used as a mean to construct a classifier system that copes with incomplete, noisy and chaotic data. ©2008 IEEE.