## Abstract

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of real world data.

Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000 |

Publisher | Neural information processing systems foundation |

ISBN (Print) | 0262122413, 9780262122412 |

State | Published - 1 Jan 2001 |

Event | 14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States Duration: 27 Nov 2000 → 2 Dec 2000 |

### Other

Other | 14th Annual Neural Information Processing Systems Conference, NIPS 2000 |
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Country | United States |

City | Denver, CO |

Period | 27/11/00 → 2/12/00 |