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
EditorsChulhee Lee, Bormin Huang, Chein-I Chang
PublisherSPIE
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

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9874
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

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

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Woo, S., & Lee, C. (2016). Imbalanced data classification using reduced multivariate polynomial. In C. Lee, B. Huang, & C-I. Chang (Eds.), Remotely Sensed Data Compression, Communications, and Processing XII [98740N] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9874). SPIE. https://doi.org/10.1117/12.2224452