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Author: search.filters.author.Yang, Wei-PangSubject: search.filters.subject.classification

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    Designing a classifier by a layered multi-population genetic programming approach
    (2007-08-01)
    Lin, Jung-Yi
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    Ke, Hao-Ren
    ;
    Chien, Been-Chian
    ;
    Yang, Wei-Pang
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    資訊工程學系
    This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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    A discretization algorithm based on Class-Attribute Contingency Coefficient
    (2008-02-01)
    Tsai, Cheng-Jung
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    Lee, Chien-I.
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    Yang, Wei-Pang
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. In this paper, we propose a static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification. As to the execution time of discretization, the number of generated rules, and the training time of C5.0, our approach also achieved promising results. (c) 2007 Elsevier Inc. All rights reserved.
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    An approach to mining the multi-relational imbalanced database
    (2008-05-04)
    Lee, Chien-I
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    Tsai, Cheng-Jung
    ;
    Wu, Tong-Qin
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database. (c) 2007 Elsevier Ltd. All rights reserved.