Pattern Recognition Letters
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Spanning SVM Tree for Personalized Transductive Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
DMCS: Dual-Model Classification System and Its Application in Medicine
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A Transductive Neuro-Fuzzy Force Control: An Ethernet-Based Application to a Drilling Process
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A transductive neuro-fuzzy controller: application to a drilling process
IEEE Transactions on Neural Networks
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
TTLSC – transductive total least square model for classification and its application in medicine
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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This paper introduces a novel neural fuzzy inference method-NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS-dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively-using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems.