【摘 要】
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A new classification model,the fuzzy hy-brid twin support vector machine(TWSVM),namely FHTWSVM,is proposed by combining the fuzzy TWS-VM and the hypersphere support vector machine(SVM).The hypersphere SVM is utilized for generating the hy-perspheres for t
【机 构】
：
College of Mathematics and Systems Science,Shandong University of Science andTechnology,Qingdao 2665
论文部分内容阅读
A new classification model,the fuzzy hy-brid twin support vector machine(TWSVM),namely FHTWSVM,is proposed by combining the fuzzy TWS-VM and the hypersphere support vector machine(SVM).The hypersphere SVM is utilized for generating the hy-perspheres for the positive and negative class with the smallest possible radius,so that the hyperspheres can con-tain as many samples as possible.The samples which the hyperspheres cover form a new sample set.Furthermore a distance-based fuzzy function is utilized to calculate the fuzzy factors for the samples.Finally FHTWSVM is used to train all samples with the parameters optimized by grid search.This method can maximize intra-class cluster-ing for noise removal and reduce the influence of outliers.To demonstrate the superiority of the performance of FHTWSVM over other classifiers,e.g.,KNN,RF,Bayesian,TWSVM,AdaBoost and XGBoost,a series of experiments is conducted using eight gene expression datasets.The evaluation results show that the proposed approach can improve the classification performance as well as reduce prediction errors for the datasets.
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