An n-ary [lambda]-averaging based similarity classifier - Kurama, Onesfole; Luukka, Passi; Collan, Mikael (Zielona Góra: Uniwersytet Zielonogórski)
We introduce a new n-ary [lambda] similarity classifier that is based on a new n-ary [lambda]-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far [lambda]-averaging has been used only in binary aggregation. Here the [lambda]-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.
Keywords: similarity classifier with [lambda]-averaging; n-ary [lambda]-averaging operator; n-ary t-norm; n-ary t-conorm; classification