Jong Hoon Kim, Hayoung Oh

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 11, pp. 455-464, Nov. 2022
https://doi.org/10.3745/KTSDE.2022.11.11.455,   PDF Download:
Keywords: Imbalanced Dataset, Predictive Performance, Bagging, Out-of-Distribution(OoD) Detection
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.

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