Instance-based classifiers to discover the gradient of typicality in data

Authors:
Francesco Gagliardi
Affiliations:
Department of Philosophy, "La Sapienza" University of Rome, Roma, Italy
Venue:
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Year:
2011

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Abstract

One of the aims of machine learning and data mining regards the problem of discovering useful and interesting knowledge from data. Usually instance-based (IB) classifiers are considered unsuitable for knowledge extraction tasks. Conversely in this paper we consider the families of IB classifiers based on prototype methods and on nearest-neighbours and we show that some hybrid IB classifiers can infer a mixture of representative instances, varying from abstracted prototypes to previous observed atypical exemplars, which can be used to discover the "typicality structure" of learnt categories. Experimental results show that one of the proposed hybrid classifiers "the Prototype exemplar learning classifier", detects a concise and meaningful set of representative instances varying from prototypical ones to atypical ones, which form a gradient of typicality. This kind of class representations cohere with theories developed in cognitive science about how human mind classifies.