NDLI: GAWDN-NFIS: Neural-Fuzzy Inference System with a Genetic Algorithm Based on Weighted Data Normalization and Its Application in Medicine

Content Provider	IEEE Xplore Digital Library
Author	Qun Song Tianmin Ma
Copyright Year	2007
Description	Author affiliation: Auckland Univ. of Technol., Auckland (Qun Song; Tianmin Ma)
Abstract	This paper introduces an approach of neural-fuzzy inference system (NFIS) with a genetic algorithm (GA) based on weighted data normalization (WDN) and its application in a medical decision support system. The WDN method optimizes the data normalization ranges for the input variables of the neural-fuzzy inference system and a genetic algorithm is used as part of the WDN method. A steepest descent algorithm (BP) is used for NFIS learning on the normalized data set. The derived weights have the meaning of feature importance and can be used for feature selection to decrease the number of input variables. The GAWDN-NFIS is illustrated on the case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This approach can also be applied to other distance-based, prototype learning neural network or fuzzy inference models.
Starting Page	603
Ending Page	607
File Size	101341
Page Count	5
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Decision support systems Input variables Neural networks Optimization methods Prototypes Fuzzy neural networks Inference algorithms Genetic algorithms Fuzzy systems
Content Type	Text
Resource Type	Article

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