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A Preliminary MML Linear Classifier using Principal Components for Multiple Classes
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BibTeX
@MISC{Kornienko_apreliminary,
author = {Lara Kornienko and David W. Albrecht and David L. Dowe and Lara Kornienko and David W. Albrecht and David L. Dowe},
title = {A Preliminary MML Linear Classifier using Principal Components for Multiple Classes},
year = {}
}
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Abstract
In this paper we improve on the supervised classification method developed in Kornienko et al. (2002) by the introduction of Principal Components Analysis to the inference process. We also extend the classifier from dealing with binomial (two-class) problems only to multinomial (multi-class) problems. The application to which the MML criterion has been applied in this paper is the classification of objects via a linear hyperplane, where the objects are able to come from any multi-class distribution. The inclusion of Principal Component Analysis to the original inference scheme reduces the bias present in the classifier’s search technique. Such improvements lead to a method which, when compared against three commercial Support Vector Machine (SVM) classifiers on Binary data, was found to be as good as the most successful SVM tested. Furthermore, the new scheme is able to classify objects of a multiclass distribution with just one hyperplane, whereas SVMs require several hyperplanes.
Keyphrases
multiple class preliminary mml linear classifier principal component principal component analysis bias present new scheme whereas svms require several hyperplanes successful svm multi-class distribution inference process classifier search technique mml criterion binary data original inference scheme supervised classification method commercial support vector machine linear hyperplane multiclass distribution
