Nearest neighbor classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The nearest neighbor rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine teaming to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of data sets.


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    Title :

    LDA/SVM driven nearest neighbor classification


    Contributors:
    Jing Peng, (author) / Heisterkamp, D.R. (author) / Dai, H.K. (author)


    Publication date :

    2001-01-01


    Size :

    638248 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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