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 en
ISSN 1872-7565 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 是
國別 IRL
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107031 )