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Growing a tree classifier with imprecise data
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DOI：10.1016/S0167-8655(00)00040-4.png)
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Abstract
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Symbolic data analysis proposes a general framework to extend usual data analysis methods to more complex data called symbolic objects. The prediction problem for symbolic objects is defined: it is seen to be a generalization of the prediction for standard data. An algorithm of tree-growing is developed for probabilistically imprecise data. The new algorithm is presented as a procedure for extracting knowledge from data of a more general type than standard data. Two data sets, respectively, based on categorical and continuous variables, are treated in detail. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
classification tree
supervised learning
symbolic data analysis
probabilistically imprecise data
soft recursive partition
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