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http://dx.doi.org/10.3745/KTSDE.2022.11.11.455

Boosting the Performance of the Predictive Model on the Imbalanced Dataset Using SVM Based Bagging and Out-of-Distribution Detection  

Kim, Jong Hoon (LX하우시스)
Oh, Hayoung (성균관대학교 인공지능융합학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.11, 2022 , pp. 455-464 More about this Journal
Abstract
There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.
Keywords
Imbalanced Dataset; Predictive Performance; Bagging; Out-of-Distribution(OoD) Detection;
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Times Cited By KSCI : 3  (Citation Analysis)
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