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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107031


    Title: A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan
    Authors: Wang, K-J;Adrian, A-M;Chen, K-H;Wang, K-M
    Keywords: Artificial immune recognition system;Brain metastasis;Imbalance dataset;Lung cancer;Borderline-synthetic minority over sampling technique
    Date: 2015-04-01
    Issue Date: 2016-08-15
    Publisher: Elsevier Ireland Ltd.
    Abstract: Classifying imbalanced data in medical informatics is challenging. Motivated by this issue, this study develops a classifier approach denoted as BSMAIRS. This approach combines borderline synthetic minority oversampling technique (BSM) and artificial immune recognition system (AIRS) as global optimization searcher with the nearest neighbor algorithm used as a local classifier. Eight electronic medical datasets collected from University of California, Irvine (UCI) machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS. Comparisons with several well-known classifiers were conducted based on accuracy, sensitivity, specificity, and G-mean. Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems. To further confirm its performance, BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database, Taiwan. This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer.
    Relation: Computer Methods and Programs in Biomedicine 119(2), pp.63-76
    DOI: 10.1016/j.cmpb.2015.03.003
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

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