### Abstract

Rare category detection is an open challenge in machine learning. It plays the central role in applications such as detecting new financial fraud patterns, detecting new network malware, and scientific discovery. In such cases rare categories are hidden among huge volumes of normal data and observations. In this paper, we propose a new method for rare category detection named SEDER, which requires no prior information about the data set. It implicitly performs semiparametric density estimation using specially designed exponentially families, and then picks the examples for labeling where the neighborhood density changes the most. SEDER can work in the cases where the data is not separable. Its unique feature over all existing methods lies in its prior-free nature, i.e. it does not require any prior information about the data set (e.g. the number of classes, the proportion of the different classes, etc.). Therefore, it is more suitable for real applications. Experimental results on both synthetic and real data sets demonstrate the superiority of SEDER.

Original language | English (US) |
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Title of host publication | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 |

Pages | 154-162 |

Number of pages | 9 |

State | Published - Dec 1 2009 |

Externally published | Yes |

Event | 9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States Duration: Apr 30 2009 → May 2 2009 |

### Publication series

Name | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics |
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Volume | 1 |

### Other

Other | 9th SIAM International Conference on Data Mining 2009, SDM 2009 |
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Country | United States |

City | Sparks, NV |

Period | 4/30/09 → 5/2/09 |

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Software
- Applied Mathematics

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## Cite this

*Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133*(pp. 154-162). (Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics; Vol. 1).