Training Classifiers for Tree-structured Categories with Partially Labeled Data

Authors: Ortega-Moral, M.¹; Gutiérrez-González, D.; De-Pablo, M.; Cid-Sueiro, J.

Source: The Journal of VLSI Signal Processing, Volume 48, Numbers 1-2, August 2007 , pp. 53-65(13)

Publisher: Springer

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Abstract:

In this paper we propose a new method for training classifiers for multi-class problems when classes are not (necessarily) mutually exclusive and may be related by means of a probabilistic tree structure. It is based on the definition of a Bayesian model relating network parameters, feature vectors and categories. Learning is stated as a maximum likelihood estimation problem of the classifier parameters. The proposed algorithm is specially suited to situations where each training sample is labeled with respect to only one or part of the categories in the tree. Our experiments on information retrieval scenarios show the advantages of the proposed method.

Keywords: training classifier; Bayesian model; probabilistic tree structure

Document Type: Research article

DOI: 10.1007/s11265-006-0008-7

Affiliations: 1: Email: ortegam@tsc.uc3m.es

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