Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

BMC Bioinformatics

Ying-Lian Gao¹  Liang-Rui Ren²  Junliang Shang²  Jin-Xing Liu²  Chun-Hou Zheng³ 

[1] Qufu Normal University Library, Qufu Normal University, 276826, Rizhao, China;School of Computer Science, Qufu Normal University, 276826, Rizhao, China;School of Computer Science, Qufu Normal University, 276826, Rizhao, China;College of Computer Science and Technology, Anhui University, 230601, Hefei, China;

关键词: Extreme learning machine;    Correntropy induced loss;    Supervised learning;    Bioinformatics;   

DOI  :  10.1186/s12859-020-03790-1

来源: Springer

【 摘 要 】

BackgroundAs a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM.ResultsIn this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM.ConclusionsThe classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

【 授权许可】

CC BY