An adaptable Gaussian neuro-fuzzy classifier

Authors:
Minas Pertselakis;Dimitrios Frossyniotis;Andreas Stafylopatis
Affiliations:
National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

The concept of semantic and context aware intelligent systems provides a vision for the Information Society where the emphasis lays on computing applications that can sense context from the people and the environment and wrap that knowledge into adaptable behavior. In this framework the proper and automatic classification of data gathered by sensors is of major importance. Our approach describes a model that operates as a self-evaluating classifier using on-line re-clustering, addressing adequately the basic issues of modern demands. The novelty of the model lies in a flexible and efficient initialization technique that first partitions the data space utilizing Gaussian distributions and then merges clusters so as to produce an effective partitioning.