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Proceedings Paper

Classifying imbalanced data using an Svm ensemble with k-means clustering in semiconductor test process
Author(s): Eun-mi Park; Jee-Hyong Lee
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Paper 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.

Paper Details

Date Published: 24 December 2013
PDF: 4 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672D (24 December 2013); doi: 10.1117/12.2052973
Show Author Affiliations
Eun-mi Park, Sungkyunkwan Univ. (Korea, Republic of)
Jee-Hyong Lee, Sungkyunkwan Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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