淡江大學機構典藏:Item 987654321/107028
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    题名: A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients
    作者: Wang, K.-J.;Makond, B.;Chen, K.-H.
    关键词: Breast cancer;Classification;Oversampling technique;Particle swarm optimization;Synthetic minority
    日期: 2014-07-01
    上传时间: 2016-08-15
    出版者: Elsevier BV
    摘要: In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.
    關聯: Applied Soft Computing 20, pp.15-24
    DOI: 10.1016/j.asoc.2013.09.014
    显示于类别:[企業管理學系暨研究所] 期刊論文

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