scholarly journals A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

2011 ◽  
Vol 19 (1) ◽  
pp. 105-115 ◽  
Author(s):  
Xiaowei Yang ◽  
Guangquan Zhang ◽  
Jie Lu ◽  
Jun Ma
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Classification Problems ◽  
Fuzzy Support Vector Machine ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means

2021 ◽  
pp. 1-17
Author(s):  
Ming-Ai Li ◽  
Ruo-Tu Wang ◽  
Li-Na Wei
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Joint Optimization ◽  
Cluster Center ◽  
Support Vector ◽  
Classification Problems ◽  
Fuzzy Support Vector Machine ◽  
Local Optima ◽  
Fuzzy C Means ◽  
Initial Cluster

BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima. OBJECTIVE: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM). METHOD: The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs. RESULTS: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively. CONCLUSION: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.

scholarly journals Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

2021 ◽  
Vol 9 (3) ◽  
pp. 618-629
Author(s):  
Hoda Khanali ◽  
Babak Vaziri
Keyword(s):  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Noisy Data ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Fuzzy C Means ◽  
Computational Speed ◽  
Fuzzy C Means Clustering ◽  
High Degree

Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

scholarly journals A New Multi-class Fuzzy Support Vector Machine Algorithm

2014 ◽  
pp. 153-164 ◽  
Author(s):  
Friedhelm Schwenker ◽  
Markus Frey ◽  
Michael Glodek ◽  
Markus Kächele ◽  
Sascha Meudt ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine
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