Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM

• Published in: Chinese Journal of Electronics Vol. 31; no. 1; pp. 99 - 106
• Main Authors: DUAN, Hua, FENG, Tong, LIU, Songning, ZHANG, Yulin, SU, Jionglong
• Format: Journal Article
• Language: English
• Published: Beijing, China Editorial Office of Chinese Journal of Electronics 01.01.2022
• Subjects: Binary classification; Fuzzy factor; Grid search; Hybrid twin SVM
• ISSN: 1022-4653; 2075-5597
• DOI: 10.1049/cje.2020.00.260

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.