In this paper, in order to improve recognition rate of the Jing Hua honey by adding different proportions of glucose, the pattern recognition methods of Radial Basis Function (RBF), Fuzzy k-nearest neighbor algorithm (FKNN) and Fuzzy Adaptive Resonance Theory MAP (Fuzzy ARTMAP) were used to classify the different honey adulterated proportion. The result shows that the recognition effect by usin...

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance KNN disadvantaged by presence outliers in small sample size datasets and its deteriorates with class imbalance. We propose local Bonferroni Mean Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) assigns label query dependent neare...

This study proposes an efficient non-parametric classifier for bankruptcy prediction using an adaptive fuzzy k -nearest neighbor (FKNN) method, where the nearest neighbor k and the fuzzy strength parameter m are adaptively specified by the particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is utilized to choose the most discriminative ...

In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy ...

The edited technique is of great importance in pattern recognition. The classical edited fuzzy technique use fuzzy k nearest neighbors(FKNN) to take out some useless samples which was classified erroneously in the editing process. In this paper, a proposed edited fuzzy k nearest neighbors based on threshold is developed, which not only consider the maximum membership value but also consider tha...

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each samp...

In this paper, we present an effective and efficient diagnosis system based on particle swarm optimization (PSO) enhanced fuzzy k-nearest neighbor (FKNN) for Parkinson’s disease (PD) diagnosis. In the proposed system, named PSO–FKNN, both the continuous version and binary version of PSO were used to perform the parameter optimization and feature selection simultaneously. On the one hand, the ne...

In this paper, a reformative scatter difference discriminant criterion (SDDC) with fuzzy set theory is studied. The scatter difference between between-class and within-class as discriminant criterion is effective to overcome the singularity problem of the within-class scatter matrix due to small sample size problem occurred in classical Fisher discriminant analysis. However, the conventional SD...