## Abstract

Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to G-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.

Original language | English (US) |
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Pages (from-to) | 232-249 |

Number of pages | 18 |

Journal | Pattern Recognition |

Volume | 90 |

DOIs | |

State | Published - Jun 2019 |

## Keywords

- Class imbalance
- Minority class
- Random forests
- Response-based sampling
- Weighted Bayes classifier

## ASJC Scopus subject areas

- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence