Volume 31 Issue 1
Jan.  2022
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DUAN Hua, FENG Tong, LIU Songning, ZHANG Yulin, SU Jionglong. Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM[J]. Chinese Journal of Electronics, 2022, 31(1): 99-106. doi: 10.1049/cje.2020.00.260
Citation: DUAN Hua, FENG Tong, LIU Songning, ZHANG Yulin, SU Jionglong. Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM[J]. Chinese Journal of Electronics, 2022, 31(1): 99-106. doi: 10.1049/cje.2020.00.260

Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM

doi: 10.1049/cje.2020.00.260
Funds:  This work was 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), and National Statistical Science Research Project in 2019 (2019LY49)
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  • Author Bio:

    received the Ph.D. degree in applied mathematics from Shanghai Jiaotong University, Shanghai, China, in 2008. She is currently a Professor with Shandong University of Science and Technology, Qingdao, China. Her current research interests include Petri nets, process mining, and machine learning. (Email: huaduan59@163.com)

    is currently a master candidate of mathematics and systems science in Shandong University of Science and Technology, Qingdao, China. His research interests include bioinformatics and deep learning. (Email: fengtong_666@163.com)

    is studying for a bachelor’s degree at Shandong University of Science and Technology, Qingdao, China. His current research interests include big data and machine learning. (Email: lsongning@163.com)

    (corresponding author) received the Ph.D. degree in computer software and theory from Shandong University of Science and Technology, Qingdao, China. He is currently an Associate Professor at College of Mathematics and Systems Science, Shandong University of Science and Technology. His research interests include bioinformatics and system biology. (Email: zhangyulin@sdust.edu.cn)

    holds a Ph.D. degree in statistics (Warwick) and a Ph.D. degree in automatic control and systems engineering (Sheffield). He is currently the Deputy Dean of School of Artificial Intelligence and Advanced Computing, XJTLU Entrepreneur College (Taicang). His research interest include bioinformatics, artificial intelligence, and medical image processing. (Email: Jionglong.Su@xjtlu.edu.cn)

  • Received Date: 2020-08-21
  • Accepted Date: 2021-03-31
  • Available Online: 2021-10-19
  • Publish Date: 2022-01-05
  • 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, 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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