An Iterative Undersampling of Extremely Imbalanced Data Using CSVM

摘要

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.