Abstract

Faculty of Engineering and Applied Science Department of Electronics and Computer Science Doctor of Philosophy Class-dependent features and multicategory classification by Alex Bailey The problem of pattern classification is considered for the case of multicategory classification where the number of classes, k, is greater than two. Many classification algorithms are in fact 2-class classifiers and are generalised to solve k-class problems. Which classifiers are naturally multicategory and the nature of the generalisation of a 2-class classifier to k classes is not often investigated. A thorough analysis of multicategory classification is given in this thesis which provides a new taxonomy of popular classification algorithms, and goes on to derive these from a probabilistic viewpoint. A clear distinction is made between classifiers that partition the input space and those that partition the set of k classes. Of the classifiers which partition the set of classes, the one-of-n, pairwise and hierarchical methods of decomposition are shown to be equivalent in the knowledge of the true data distributions. The scaling properties of these algorithms are analysed for increasing k. The effects of learning models on finite data are then investigated to show the practical differences between each decomposition.

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