Imbalanced data classification using reduced multivariate polynomial

Seongyoun Woo, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationRemotely Sensed Data Compression, Communications, and Processing XII
PublisherSPIE
Volume9874
ISBN (Electronic)9781510601154
DOIs
Publication statusPublished - 2016 Jan 1
EventRemotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States
Duration: 2016 Apr 202016 Apr 21

Other

OtherRemotely Sensed Data Compression, Communications, and Processing XII
CountryUnited States
CityBaltimore
Period16/4/2016/4/21

Fingerprint

Data Classification
Multivariate Polynomials
polynomials
Polynomials
minorities
Classifiers
Ridge Regression
Class
classifiers
learning
ridges
regression analysis
Maximise
Classify
Classifier
Experimental Results

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

Woo, S., & Lee, C. (2016). Imbalanced data classification using reduced multivariate polynomial. In Remotely Sensed Data Compression, Communications, and Processing XII (Vol. 9874). [98740N] SPIE. https://doi.org/10.1117/12.2224452
Woo, Seongyoun ; Lee, Chulhee. / Imbalanced data classification using reduced multivariate polynomial. Remotely Sensed Data Compression, Communications, and Processing XII. Vol. 9874 SPIE, 2016.
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Woo, S & Lee, C 2016, Imbalanced data classification using reduced multivariate polynomial. in Remotely Sensed Data Compression, Communications, and Processing XII. vol. 9874, 98740N, SPIE, Remotely Sensed Data Compression, Communications, and Processing XII, Baltimore, United States, 16/4/20. https://doi.org/10.1117/12.2224452

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 proceedingConference contribution

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Woo S, Lee C. Imbalanced data classification using reduced multivariate polynomial. In Remotely Sensed Data Compression, Communications, and Processing XII. Vol. 9874. SPIE. 2016. 98740N https://doi.org/10.1117/12.2224452