@inproceedings{5f5ad67c8a574902a358e7370a5f2b50,
title = "An iterative undersampling of extremely imbalanced data using CSVM",
abstract = "Semiconductor is a major component of electronic devices and is required very high reliability and productivity. If defective chip predict in advance, the product quality will be improved and productivity will increases by reduction of test cost. However, the performance of the classifiers about defective chips is very poor due to semiconductor data is extremely imbalance, as roughly 1:1000. In this paper, the iterative undersampling method using CSVM is employed to deal with the class imbalanced. The main idea is to select the informative majority class samples around the decision boundary determined by classify. Our experimental results are reported to demonstrate that our method outperforms the other sampling methods in regard with the accuracy of defective chip in highly imbalanced data.",
keywords = "Cost-sensitive Support Vector Machin, Heuristic undersampling method, imbalanced data, semiconductor",
author = "Lee, \{Jong Bum\} and Lee, \{Jee Hyong\}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 7th International Conference on Machine Vision, ICMV 2014 ; Conference date: 19-11-2014 Through 21-11-2014",
year = "2015",
doi = "10.1117/12.2181517",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Branislav Vuksanovic and Jianhong Zhou and Antanas Verikas and Petia Radeva",
booktitle = "Seventh International Conference on Machine Vision, ICMV 2014",
}