A quadratic mean based supervised learning model for managing data skewness

Wei Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In this paper, we study the problem of data skewness. A data set is skewed/imbalanced if its dependent variable is asymmetrically distributed. Dealing with skewed data sets has been identified as one of the ten most challenging problems in data mining research. We address the problem of class skewness for supervised learning models which are based on optimizing a regularized empirical risk function. These include both classification and regression models for discrete and continuous dependent variables. Classical empirical risk minimization is akin to minimizing the arithmetic mean of prediction errors, in which approach the induction process is biased towards the majority class for skewed data. To overcome this drawback, we propose a quadratic mean based learning framework (QMLearn) that is robust and insensitive to class skewness. We will note that minimizing the quadratic mean is a convex optimization problem and hence can be efficiently solved for large and high dimensional data. Comprehensive experiments demonstrate that the QMLearn model significantly outperforms existing statistical learners including logistic regression, support vector machines, linear regression, support vector regression and quantile regression etc.

Original languageEnglish
Title of host publicationProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PublisherSociety for Industrial and Applied Mathematics Publications
Pages188-198
Number of pages11
ISBN (Print)9780898719925
DOIs
StatePublished - 2011
Externally publishedYes
Event11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States
Duration: Apr 28 2011Apr 30 2011

Publication series

NameProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Conference

Conference11th SIAM International Conference on Data Mining, SDM 2011
Country/TerritoryUnited States
CityMesa, AZ
Period04/28/1104/30/11

Keywords

  • Convex optimization
  • Data skewness
  • Quadratic mean

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