Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM
Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM作者机构：College of Mathematics and Systems Science Shandong University of Science and Technology School of AI and Advanced Computing XJTLU Entrepreneur College (Taicang) Xi'an Jiaotong-Liverpool University
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2022年第31卷第1期
页 面：99-106页
核心收录：
学科分类：0710[理学-生物学] 0711[理学-系统科学] 1002[医学-临床医学] 07[理学] 08[工学] 081104[工学-模式识别与智能系统] 070104[理学-应用数学] 100214[医学-肿瘤学] 0701[理学-数学] 0811[工学-控制科学与工程] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China (U1931207, 61702306) Sci.&Tech. Development Fund of Shandong Province of China (ZR2017BF015, ZR2017MF027) the Humanities and Social Science Research Project of the Ministry of Education (18YJAZH017) the Taishan Scholar Program of Shandong Province,SDUST Research Fund (2015TDJH102, 2019KJN024) National Statistical Science Research Project in 2019 (2019LY49)
主 题：Binary classification Hybrid twin SVM Fuzzy factor Grid search
摘 要：A new classification model, the fuzzy hybrid twin support vector machine(TWSVM), namely FHTWSVM, is proposed by combining the fuzzy TWSVM and the hypersphere support vector machine(SVM).The hypersphere SVM is utilized for generating the hyperspheres for the positive and negative class with the smallest possible radius, so that the hyperspheres can contain 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 clustering 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, Ada Boost 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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