Transductive knowledge based fuzzy inference system for personalized modeling

Authors:
Qun Song;Tianmin Ma;Nikola Kasabov
Affiliations:
Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

This paper introduces a novel transductive knowledge based fuzzy inference system (TKBFIS) and its application for creating personalized models. In transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. A higher-order TSK type fuzzy inference engine is applied in TKBFIS. Some existing formulas or equations, which are used to represent the knowledge and usually have a non-linear form, are taken as consequent parts of the fuzzy rules. The TKBFIS uses a gradient descent algorithm for its training. In this paper, the TKBFIS is illustrated with a case study of personalized modeling for renal function estimation of patients and the result is compared with other transductive or inductive methods.