典型文献
Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
DUAN Hua;FENG Tong;LIU Songning;ZHANG Yulin;SU Jionglong
作者机构：
College of Mathematics and Systems Science,Shandong University of Science andTechnology,Qingdao 266590,China;School of AI and Advanced Computing,XJTLU Entrepreneur College(Taicang),Xi'an Jiaotong-Liverpool University,Suzhou 215123,China
文献出处：
引用格式：
[1]DUAN Hua;FENG Tong;LIU Songning;ZHANG Yulin;SU Jionglong-.Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM)[J].电子学报（英文）,2022(01):99-106
A类：
FHTWSVM,perspheres,hyperspheres
B类：
Tumor,Classification,Gene,Expression,Data,by,Fuzzy,Hybrid,Twin,new,classification,model,fuzzy,twin,support,vector,machine,namely,proposed,combining,utilized,generating,positive,negative,smallest,possible,radius,so,that,can,tain,many,samples,which,cover,Furthermore,distance,function,calculate,factors,Finally,used,train,parameters,optimized,grid,search,This,method,maximize,intra,cluster,noise,removal,reduce,influence,outliers,To,demonstrate,superiority,performance,other,classifiers,KNN,RF,Bayesian,AdaBoost,XGBoost,series,experiments,conducted,using,eight,expression,datasets,evaluation,results,show,approach,improve,well,prediction,errors
AB值：
0.487315
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