Classifying imbalanced data using an Svm ensemble with k-means clustering in semiconductor test process

Eun-mi Park; Jee-Hyong Lee

doi:10.1117/12.2052973

24 December 2013 Classifying imbalanced data using an Svm ensemble with k-means clustering in semiconductor test process

Eun-mi Park, Jee-Hyong Lee

Author Affiliations +

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672D (2013) https://doi.org/10.1117/12.2052973
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

In the semiconductor manufacturing process, it is important to predict defective chips in advance for reduction of test cost and early stabilization of the production process. However, highly imbalanced datasets in the semiconductor test process degrade the performance of prediction. In order to enhance an SVM Ensemble, this study presents an improved methodology using the K-means, which clusters the majority class and the minority class before training an SVM. A result of the experiment with the actual data of the semiconductor test process is reported to demonstrate that our approach outperforms other methods in terms of classifying the imbalanced dataset.

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Eun-mi Park and Jee-Hyong Lee "Classifying imbalanced data using an Svm ensemble with k-means clustering in semiconductor test process", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672D (24 December 2013); https://doi.org/10.1117/12.2052973

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