Authors:
Hossein Pazhoumand-Dar
;
Chiou-Peng Lam
and
Martin Masek
Affiliation:
Edith Cowan University, Australia
Keyword(s):
Fuzzy Membership Functions, Variable Bandwidth Mean-shift, Fuzzy Logic, Activities of Daily Living, Abnormality Detection, Robust Statistics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Fuzzy Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust statistics is presented for generating fuzzy membership functions from data. The approach takes an attribute and automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach also works out the associated parameters. The investigation here examines the application of approach using the triangular membership function. Results from partitioning of attributes confirm that the generated membership functions can better separate the underlying distributions when compared to a number of other techniques. Classification performance of fuzzy rule sets produced using four different methods of parameterizing the associated attributes is examined. We observed that the classifier constructed using the proposed method of generating membership function outperformed the 3 other classifiers that had used other methods of
parameterizing the attributes.
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