Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients

被引：0
|
作者：
Zieba, Maciej [1 ]
Tomczak, Jakub M. [1 ]
Lubicz, Marek [1 ]
Swiatek, Jerzy [1 ]
机构：
[1] Wroclaw Univ Technol, Fac Comp Sci & Management, PL-50370 Wroclaw, Poland
来源：
APPLIED SOFT COMPUTING | 2014年 / 14卷
关键词：
Imbalanced data; Boosted SVM; Decision rules; Post operative life expectancy prediction; SUPPORT VECTOR MACHINES; IN-HOSPITAL DEATH; 2 SCORING SYSTEMS; THORACIC-SURGERY; RISK STRATIFICATION; MORBIDITY; MORTALITY; CLASSIFICATION; KNOWLEDGE; SELECTION;
D O I：
10.1016/j.asor.2013.07.016
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present boosted SVM dedicated to solve imbalanced data problems. Proposed solution combines the benefits of using ensemble classifiers for uneven data together with cost-sensitive support vectors machines. Further, we present oracle-based approach for extracting decision rules from the boosted SVM. In the next step we examine the quality of the proposed method by comparing the performance with other algorithms which deal with imbalanced data. Finally, boosted SVM is used for medical application of predicting post-operative life expectancy in the lung cancer patients. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：99 / 108
页数：10
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