Integrating models of discrimination and characterization

Article type: Research Article

Authors: Davidsson, Paul^{; *}

Affiliations: Department of Computer Science, University of Karlskrona/Ronneby, S-372 25 Ronneby, Sweden

Correspondence: [*] Tel.: +46-457-78784; fax: +46-457-27125; e-mail: paul.davidsson@ipd.hk-r.se

Abstract: It is argued that in applications of concept learning from examples where not every possible category of the domain is present in the training set (i.e., many real world applications), classification performance can be improved by integrating suitable discriminative and characteristic models of classification. The suggested approach is to first discriminate between the categories present in the training set and then characterize each of these categories against all possible categories. To show the viability of this approach, a number of different discriminators and characterizers are integrated and tested. In particular, a novel characterization method that makes use of the information about the statistical distribution of feature values that can be extracted from the training examples is used. By using this method it is possible to control the degree of generalization and to deal with dependencies among features.

Keywords: Concept learning, Learning from examples, Discrimination, Characterization

DOI: 10.3233/IDA-1999-3202

Journal: Intelligent Data Analysis, vol. 3, no. 2, pp. 95-109, 1999