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The paper describes a novel computational tool for multiple concept learning. Unlike previous approaches, whose major goal is prediction on unseen instances rather than the legibility of the output, our MPD (Maximally Parsimonious Discrimination) program emphasizes the conciseness and intelligibility of the resultant class descriptions, using three intuitive simplicity criteria to this end. We illustrate MPD with applications in componential analysis (in lexicology and phonology), language typology, and speech pathology. . | A Procedure for Multi-Class Discrimination and some Linguistic Applications Vladimir Pericliev Institute of Mathematics Informatics Acad. G. Bonchev Str. bl. 8 1113 Sofia Bulgaria periSmath.acad.bg Abstract The paper describes a novel computational tool for multiple concept learning. Unlike previous approaches whose major goal is prediction on unseen instances rather than the legibility of the output our MPD Maximally Parsimonious Discrimination program emphasizes the conciseness and intelligibility of the resultant class descriptions using three intuitive simplicity criteria to this end. We illustrate MPD with applications in componential analysis in lexicology and phonology language typology and speech pathology. 1 Introduction A common task of knowledge discovery is multiple concept learning in which from multiple given classes i.e. a typology the profiles of these classes are inferred such that every class is contrasted from every other class by feature values. Ideally good profiles besides making good predictions on future instances should be concise intelligible and comprehensive i.e. yielding all alternatives . Previous approaches like ID3 Quinlan 1983 or C4.5 Quinlan 1993 which use variations on greedy search i.e. localized best-next-step search typically based on information-gain heuristics have as their major goal prediction on unseen instances and therefore do not have as an explicit concern the conciseness intelligibility and comprehensiveness of the output. In contrast to virtually all previous approaches to multi-class discrimination the MPD Maximally Parsimonious Discrimination program we describe here aims at the legibility of the resultant class profiles. To do so it 1 uses a minimal number of features by carrying out a global optimization rather than heuristic greedy search 2 produces conjunctive or nearly conjunctive profiles for the sake of intelligibility and 3 gives all alternative solutions. The first goal stems from the familiar Raúl E. .
