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Author: search.filters.author.Foitong, SombutSubject: search.filters.subject.Classification

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    Rough-mutual feature selection based on min-uncertainty and max-certainty
    (2012-01-01)
    Foitong, Sombut
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    Pinngern, Ouen
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    Attachoo, Boonwat
    Feature selection (FS) plays an important role in pattern recognition and machine learning. FS is applied to dimensionality reduction and its purpose is to select a subset of the original features of a data set which is rich in the most useful information. Most existing FS methods based on rough set theory focus on dependency function, which is based on lower approximation as for evaluating the goodness of a feature subset. However, by determining only information from a positive region but neglecting a boundary region, most relevant information could be invisible. This paper, the maximal lower approximation (Max Certainty) minimal boundary region (Mm Uncertainty) criterion, focuses on feature selection methods based on rough set and mutual infonnation which use different values among the lower approximation information and the information contained in the boundary region. The use of this idea can result in higher predictive accuracy than those obtained using the measure based on the positive region (certainty region) alone. This demonstrates that much valuable information can be extracted by using this idea. Experimental results are illustrated for discrete, continuous, and microarray data and compared with other FS methods in terms of subset size and classification accuracy. key words: rough sets, mutual information, feature selection, boundary region, classification accuracy. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.
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    Towards adapting XCS for imbalance problems
    (2008-12-01)
    Nguyen, Thach Huy
    ;
    Foitong, Sombut
    ;
    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.
    ;
    Foitong, Sombut
    ;
    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.