Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

Evolutionary under-sampling based bagging ensemble method for imbalanced data

classification

Bo SUN;Haiyan CHEN;Jiandong WANG;Hua XIE

【期刊名称】《中国高等学校学术文摘·计算机科学》 【年(卷),期】2018(012)002

【摘要】In the class imbalanced learning scenario,traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested.To solve this problem,many effective approaches have been proposed.Among them,the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods integrated with the over-sampling techniques,the cost-sensitive methods,etc.Although these under-sampling techniques promote the diversity among the generated base classifiers with the help of random partition or sampling for the majority class,they do not take any measure to ensure the individual classification performance,consequently affecting the achievability of better ensemble performance.On the other hand,evolutionary under-sampling EUS as a novel undersampling technique has been successfully applied in searching for the best majority class subset for