## Abstract

This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n^{2}) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n). This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.

Original language | English |
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Title of host publication | Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 |

Editors | R. Greiner, D. Schuurmans |

Pages | 385-392 |

Number of pages | 8 |

Publication status | Published - 2004 |

Externally published | Yes |

Event | International Conference on Machine Learning 2004 - Banff, Alta, Canada Duration: 4 Jul 2004 → 8 Jul 2004 Conference number: 21st |

### Conference

Conference | International Conference on Machine Learning 2004 |
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Abbreviated title | ICML 2004 |

Country | Canada |

City | Banff, Alta |

Period | 4/07/04 → 8/07/04 |