### 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 language | English |
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Title of host publication | Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 |

Pages | 188-198 |

Number of pages | 11 |

Publication status | Published - 1 Dec 2011 |

Event | 11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States Duration: 28 Apr 2011 → 30 Apr 2011 |

### Publication series

Name | Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 |
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### Other

Other | 11th SIAM International Conference on Data Mining, SDM 2011 |
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Country | United States |

City | Mesa, AZ |

Period | 28/4/11 → 30/4/11 |

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### Keywords

- Convex optimization
- Data skewness
- Quadratic mean

### ASJC Scopus subject areas

- Software

### Cite this

*Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011*(pp. 188-198). (Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011).