Autonomous and deterministic clustering for evidence-theoretic classifier

Authors:
Chen Li Poh;Loo Chu Kiong;M. V. C. Rao
Affiliations:
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia;Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia;Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

This paper describes an evidence-theoretic classifier which employs global k-means algorithm as the clustering method. The classifier is based on the Dempster-Shafer rule of evidence in the form of Basic Belief Assignment (BBA). This theory combines the evidence obtained from the reference patterns to yield a new BBA. Global k-means is selected as the clustering algorithm as it can overcomes the limitation on k-means clustering algorithm whose performance depends heavily on initial starting conditions selected randomly and requires the number of clusters to be specified before using the algorithm. By testing the classifier on the medical diagnosis benchmark data, iris data and Westland vibration data, one can conclude classifier that uses global k-means clustering algorithm has higher accuracy when compared to the classifier that uses k-means clustering algorithm.