TWNFI--a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling.
This paper introduces a novel transductive neuro-fuzzy inference model with weighted data normalization (TWNFI). In transductive systems a local model is developed for every new input vector, based on a certain number of data that are selected from the training data set and the closest to this vector. The weighted data normalization method (WDN) optimizes the data normalization ranges of the input variables for the model. A steepest descent algorithm is used for training the TWNFI models. The TWNFI is compared with some other widely used connectionist systems on two case study problems: Mackey-Glass time series prediction and a real medical decision support problem of estimating the level of renal function of a patient. The TWNFI method not only results in a "personalized" model with a better accuracy of prediction for a single new sample, but also depicts the most significant input variables (features) for the model that may be used for a personalized medicine.
Song
Qun
Q
Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand.
Kasabov
Nikola
N
D000465:Algorithms; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007678:Kidney Glomerulus / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012016:Reference Values; D014894:Weights and Measures / Q000592:standards*