An iterative undersampling of extremely imbalanced data using CSVM

Jong Bum Lee; Jee Hyong Lee

doi:10.1117/12.2181517

14 February 2015 An iterative undersampling of extremely imbalanced data using CSVM

Jong Bum Lee, Jee Hyong Lee

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94452B (2015) https://doi.org/10.1117/12.2181517
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

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.

Citation Download Citation

Jong Bum Lee and Jee Hyong Lee "An iterative undersampling of extremely imbalanced data using CSVM", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94452B (14 February 2015); https://doi.org/10.1117/12.2181517

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available