A fuzzy neural network for intelligent data processing
Lim, Eng Thiam
Date of Issue2005
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security (2005 : Bellingham, USA)
School of Electrical and Electronic Engineering
In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.
DRNTU::Engineering::Electrical and electronic engineering
© 2005 The International Society for Optical Engineering (SPIE). This paper was published in Proc. Society of Photo-Optical Instrumentation Engineers (SPIE), Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005 and is made available as an electronic reprint (preprint) with permission of SPIE. The paper can be found at the following official URL: [http://dx.doi.org/10.1117/12.603175]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.