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.
| Original language | English |
|---|---|
| Title of host publication | Remotely Sensed Data Compression, Communications, and Processing XII |
| Publisher | SPIE |
| Volume | 9874 |
| ISBN (Electronic) | 9781510601154 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
| Event | Remotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States Duration: 2016 Apr 20 → 2016 Apr 21 |
Other
| Other | Remotely Sensed Data Compression, Communications, and Processing XII |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 16/4/20 → 16/4/21 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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Imbalanced data classification using reduced multivariate polynomial. / Woo, Seongyoun; Lee, Chulhee.
Remotely Sensed Data Compression, Communications, and Processing XII. Vol. 9874 SPIE, 2016. 98740N.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Imbalanced data classification using reduced multivariate polynomial
AU - Woo, Seongyoun
AU - Lee, Chulhee
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84991516773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991516773&partnerID=8YFLogxK
U2 - 10.1117/12.2224452
DO - 10.1117/12.2224452
M3 - Conference contribution
AN - SCOPUS:84991516773
VL - 9874
BT - Remotely Sensed Data Compression, Communications, and Processing XII
PB - SPIE
ER -