In this paper, the use of a Fuzzy Complementary Criterion (FuzCoC) for structure learning of a neuro-fuzzy classifier arranged in layers is proposed. The FuzCoC has been recently proposed as an effective criterion for...
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ISBN:
(纸本)9781424469208
In this paper, the use of a Fuzzy Complementary Criterion (FuzCoC) for structure learning of a neuro-fuzzy classifier arranged in layers is proposed. The FuzCoC has been recently proposed as an effective criterion for feature selection. Simulation results in a large number of benchmark problems revealed the capability of this method in selecting small subsets of powerful and complementary features even in high dimensional feature sets. In this paper, the FuzCoC method is used not only to reduce the dimensions of the original feature space, but also to identify complementary generic fuzzy neuron classifiers (FNCs) arranged in layers. The chosen generic classifiers are then combined using a decision fusion operator to construct a descendant FNC at the next layer with enhanced classification capabilities. The proposed structure learning algorithm is a modified version of the groupmethod of datahandling (GMDH) algorithm which incorporates the FuzCoC method simultaneous as a pre-feature selection method and as a method to identify complementary generic classifiers to be combined in the next layer. Simulation results demonstrate the capabilities of the proposed method in building accurate neuro-fuzzy classifiers with reduced computational demands.
Experimental Software datasets describing Software projects in terms of their complexity and development time have been the subject of intensive modelling. A number of various modelling methodologies and detailed mode...
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Experimental Software datasets describing Software projects in terms of their complexity and development time have been the subject of intensive modelling. A number of various modelling methodologies and detailed modelling designs hake been proposed including neural networks and fuzzy models. The authors introduce self-organising networks (SON) that result from a synergy of fuzzy inference schemes and polynomial neural networks (PNNs). The latter has included an efficient scheme of selecting input variables of the model being realised on a basis of a groupmethod of datahandling (GMDH) algorithm. The authors discuss a detailed architecture of the SON and propose a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SON are not predetermined (as is the case in a popular topology of a multilayer perceptron). The experimental results include well-known software data such as the one describing software modules of the medical imaging system (MIS) and the NASA data set concerning software cost estimation. The experimental results reveal that the proposed model exhibits high accuracy.
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