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Elastic Net Regularized Softmax Regression Methods for Multi-subtype Classification in Cancer

Author(s):

Lin Zhang*, Yanling He, Haiting Song, Xuesong Wang, Nannan Lu, Lei Sun and Hui Liu  

Abstract:


Various regularization methods have been proposed to improve the prediction accuracy in cancer diagnosis. Elastic net regularized logistic regression has been widely adopted for cancer classification and gene selection in genetics and molecular biology but commonly is applied to binary classification. However, usually, the cancer subtypes can be more, and most likely cannot be decided precisely. Thus, an elastic net regularized softmax regression (ENRSR) for multi-classification is put forward to tackle the multiple classification issue. As an extension of elastic net regularized logistic regression, ENRSR enforces structure sparsity and ‘grouping effect’ for gene selection, which may exhibit high correlation in the data. It is demonstrated that ENRSR gains more accurate and robust performance comparing to the other 6 competing algorithms (K-means, Hierarchical Clustering, Expectation Maximization, Nonnegative Matrix Factorization, Support Vector Machine and Random Forest) in predicting cancer subtypes both on simulation data and real cancer gene expression data in terms of BCubed F metrics. Therefore, we can conclude that the proposed method is a reliable regularized softmax regression for multi-subtype classification.

Keywords:

regularization, softmax regression, elastic net, multiple classification, gene selection

Affiliation:

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, The National Laboratory of Radar Signal Processing, Xidian University,Xi`an,710071, The School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, The School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, chool of Information Engineering Yangzhou University, Yangzhou,225127, The School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116



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