Abstract
This paper aims to presenting a new algorithm to classify symbolic data. The input data for the learning step is a set of symbolic objects described by symbolic interval (or set-valued) variables. At the end of the learning step, each group is represented by a (modal) symbolic object which is described by symbolic histogram (or bar-diagram) variables. The assignment of a new observation to a group is based on a dissimilarity function which measures the difference in content and in position between them. The difference in position is measured by a context free component whereas the difference in content is measured by a context dependent component. To show the usefulness of this modal symbolic pattern classifier, a particular kind of simulated images is classified according to this approach.
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de Carvalho, F.d.A.T., de Souza, R.M.C.R., Verde, R. (2004). A Modal Symbolic Pattern Classifier. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_2
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DOI: https://doi.org/10.1007/978-3-642-17111-6_2
Publisher Name: Springer, Berlin, Heidelberg
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