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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51816

    Title: Mining associative classification rules with stock trading data - A GA-based method
    Authors: Chien,YWC;Chen,YL
    Contributors: 資訊管理學系
    Date: 2010
    Issue Date: 2012-03-27 19:06:49 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Associative classifiers are a classification system based on associative classification rules Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships Therefore, an ongoing research problem is how to build associative classifiers from numerical data In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method (C) 2010 Elsevier B.V. All rights reserved
    Appears in Collections:[資訊管理學系] 期刊論文

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