Classification of Breast Cancer Gene Data Based on gcForest
Qin Xiwen1,2*, Wang Rui1,3, Zhang Siqi1,3
1(Institute of Big Data Science, Changchun University of Technology, Changchun 130012, China) 2(Graduate School, Changchun University of Technology, Changchun 130012, China) 3(School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China)
Abstract：The classification of breast cancer gene data is of great importance in clinical medicine. Aiming at the characteristics of complex structure, high-dimensional and small samples of gene data, this paper proposes a gene data classification method based on the max-relevance and min-conditional redundancy ( mRMCR ) and multi-grained cascade forest ( gcForest ). A total of 98 data were selected from the breast cancer gene expression data set of theBroad Gene Research Institute, and each sample contained 1 213 characteristic genes. Firstly, the data are standardized, then the feature subsets are selected by using the max-relevance and min-conditional redundancy , and finally the feature subsets are classified by the gcForest. Taking random forest, support vector machine and BP neural network as comparison methods, the results show that the best classification accuracy of the proposed combination method of mRMCR and gcForest is 93.78%, which is obviously better than other methods. This method can effectively improve the classification accuracy of breast cancer gene data, and has important theoretical significance and practical value for breast cancer classification based on gene data.
秦喜文, 王芮, 张斯琪. 基于深度级联森林的乳腺癌基因数据分类研究[J]. 中国生物医学工程学报, 2022, 41(2): 177-185.
Qin Xiwen, Wang Rui, Zhang Siqi. Classification of Breast Cancer Gene Data Based on gcForest. Chinese Journal of Biomedical Engineering, 2022, 41(2): 177-185.
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