Imbalanced data classification using reduced multivariate polynomial

Seongyoun Woo; Chulhee Lee

doi:10.1117/12.2224452

19 May 2016 Imbalanced data classification using reduced multivariate polynomial

Seongyoun Woo, Chulhee Lee

Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 98740N (2016) https://doi.org/10.1117/12.2224452
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In this paper, a weighted reduced multivariate polynomial for class imbalance learning is proposed. When there is a large variation in the numbers of available class samples, class distribution is said to be imbalanced. In such cases, conventional classifiers may classify most samples as majority classes to maximize the classification accuracy, which may not be desirable in some applications. Thus, for imbalanced data classification, an additional algorithm may be required to address low representation of minority classes when the classification performance of those classes is important. We used weighted ridge regression for class imbalanced data classification. Experimental results with the UCI database show improved classification of the minority classes.

Citation Download Citation

Seongyoun Woo and Chulhee Lee "Imbalanced data classification using reduced multivariate polynomial", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740N (19 May 2016); https://doi.org/10.1117/12.2224452

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