教師資料查詢 | 類別: 期刊論文 | 教師: 陳昆皇 KUN-HUANG CHEN (瀏覽個人網頁)

標題:A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients
學年102
學期2
出版(發表)日期2014/07/01
作品名稱A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients
作品名稱(其他語言)
著者K.-J. Wang; B. Makond; K.-H. Chen
單位
出版者
著錄名稱、卷期、頁數Applied Soft Computing 20, pp.15-24
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.
關鍵字Breast cancer;Classification;Oversampling technique;Particle swarm optimization;Synthetic minority
語言英文
ISSN1568-4946;1872-9681
期刊性質國外
收錄於SCI;
產學合作
通訊作者K.-J. Wang
審稿制度
國別荷蘭
公開徵稿
出版型式,電子版,紙本
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