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The advantages of multi-classification schemes based on decomposition strategies, and especially the One-vs-One framework, have been stressed even for those algorithms that can address multiple classes. However, there is an inherent hitch for the One-vs-One learning scheme related to the decision process: the non-competent classifier problem. This issue refers to the case where a binary classifier...
Fuzzy genetics-based machine learning (FGBML) is one of the representative approaches to obtain a set of fuzzy if-then rules by evolutionary computation. A number of FGBML methods have been proposed so far. Among them, Michigan-style approaches are popular thanks to thier lower computational cost than Pittsburgh approaches. In this study, we introduce two simple modifications for our Michigan-style...
This paper studies the structure and algorithm of fuzzy pattern recognition, and proposes some improvements with its inherent limitations. In order to improve the accuracy of recognition better, this paper designs the genetic algorithm fuzzy classifier (GA-FPC) which is an optimization method of fuzzy pattern recognition using genetic algorithm. We have done some experiments on UCI datasets using...
Genetic fuzzy rule selection has been frequently used for fuzzy rule-based classifier design. A number of its variants have also been proposed in the literature. In many studies on genetic fuzzy rule selection, each antecedent condition in fuzzy rules is given for a single input variable such as “x1 is small” and “x2 is large”. As a result, each antecedent fuzzy set is defined on a single input variable...
Predictively encoded techniques are commonly used for lossless compression of images for its effectiveness of removing statistical redundancy between pixels. However, there can be large prediction errors for pixels around boundaries. In this paper, we introduce techniques commonly used in control systems to enhance the coding efficiency of predictive coding. Actually, the predictive coding system...
The purpose of this paper is based on radial basis function neural network (RBFN) to develop a self-constructing least Wilcoxon-generalized RBFN fuzzy inference system (LW-GRBFNFIS) and applied to nonlinear function approximation and chaotic time series prediction. As is well known in statistics, the resulting linear function by using the rank-based least Wilcoxon (LW) norm approximate to linear function...
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