A Unifying Framework for Classification Procedures Based on Cluster Aggregation by Choquet Integral
A unifying framework for classification procedures which makes use of clustering and utility aggregation by (a variant of) Choquet integral is introduced. The model is presented as a general framework which looks at classification as an aggregation of information induced by clusters, so that the decision to which class unlabelled points should belong is taken by considering the whole space. Classification procedures k-nearest neighbor (k-NN) and classification trees (CT) are reformulated within the proposed framework. In addition, the model can be used to define a new classification procedures. An example is provided and compared to the others when applied to two UCI datasets.
KeywordsClassification k-NN nearest neighbors decision tree classification tree Choquet integral
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