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 Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan
學年103
學期2
出版(發表)日期2015/04/01
作品名稱A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan
作品名稱(其他語言)
著者K-J Wang; A-M Adrian; K-H Chen; K-M Wang
單位
出版者
著錄名稱、卷期、頁數Computer Methods and Programs in Biomedicine 119(2), pp.63-76
摘要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.
關鍵字Artificial immune recognition system;Brain metastasis;Imbalance dataset;Lung cancer;Borderline-synthetic minority over sampling technique
語言英文
ISSN1872-7565;0169-2607
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
國別愛爾蘭
公開徵稿
出版型式,電子版,紙本
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