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Adaptive Metric Nearest Neighbor Classification (2000)
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| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 104 - 4 self |
BibTeX
@ARTICLE{Domeniconi00adaptivemetric,
author = {Carlotta Domeniconi and Jing Peng and Dimitrios Gunopulos},
title = {Adaptive Metric Nearest Neighbor Classification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2000},
volume = {24},
pages = {1281--1285}
}
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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 Chisquared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods 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 simulated and real world data. 1 Introduction In a classification problem, we are given J classes and N tra...
Keyphrases
adaptive metric nearest neighbor classification finite sample neighbor classification classification problem neighbor rule real world data modified neighborhood constant class conditional probability classification performance severe bias chisquared distance analysis high dimension neighbor classification method class conditional probability relevant feature dimension influential one
