Effective learning system techniques for human-robot interaction in service environment
Knowledge-Based Systems
Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
Fuzzy-state Q-learning-based human behavior suggestion system in intelligent sweet home
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Hybrid mammogram classification using rough set and fuzzy classifier
Journal of Biomedical Imaging
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
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To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.