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 |
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Title of host publication | Remotely Sensed Data Compression, Communications, and Processing XII |
Editors | Chulhee Lee, Bormin Huang, Chein-I Chang |
Publisher | SPIE |
ISBN (Electronic) | 9781510601154 |
DOIs | |
Publication status | Published - 2016 |
Event | Remotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States Duration: 2016 Apr 20 → 2016 Apr 21 |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 9874 |
ISSN (Print) | 0277-786X |
ISSN (Electronic) | 1996-756X |
Other
Other | Remotely Sensed Data Compression, Communications, and Processing XII |
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Country/Territory | United States |
City | Baltimore |
Period | 16/4/20 → 16/4/21 |
Bibliographical note
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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