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Author: search.filters.author.Srinil, PhaitoonSubject: search.filters.subject.Classification

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    Min-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection
    (2017-01-01)
    Foithong, Sombut
    ;
    Srinil, Phaitoon
    ;
    Pinngern, Ouen
    ;
    Attachoo, Boonwat
    Feature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but neglect a boundary region. This paper proposes a criterion of the maximal lower approximation information (Max-Certainty) and minimal boundary region information (Min-Uncertainty), based on neighborhood rough set and mutual information for evaluating the goodness of a feature subset. We combine this proposed criterion with neighborhood rough set, which is directly applicable to numerical and heterogeneous features, without involving a discretization of numerical features. Comparing it with the rough set based approaches, our proposed method improves accuracy over various experimental data sets. Experimental results illustrate that much valuable information can be extracted by using this idea. This proposed technique is demonstrated on discrete, continuous, and heterogeneous data, and is compared with other FS methods in terms of subset size and classification accuracy.
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    Towards adapting XCS for imbalance problems
    (2008-12-01)
    Nguyen, Thach Huy
    ;
    Foitong, Sombut
    ;
    Srinil, Phaitoon
    ;
    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.