L2, 1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification. (December 2020)

• Record Type: Journal Article
• Authors: Ren, Liang-Rui; Gao, Ying-Lian; Liu, Jin-Xing; Zhu, Rong; Kong, Xiang-Zhen
• Is Part Of: Computational biology and chemistry. Volume 89(2020)
• Publication Date: 2020-12
• Subjects: Extreme Learning Machine -- L2, 1-norm -- Robust -- Single-cell RNA Sequencing -- Supervised Learning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107368
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Based on L21 -norm, a robust Extreme Learning Machine method called L21 -ELM is proposed. Various benchmark datasets downloaded from the UCI database and some image datasets are used to train and test the model. The proposed L21 -ELM is applied to the classification of cancer samples and single-cell data. The proposed method not only inherits the advantages of original ELM, such as easy implementation and fast speed, but also shows better generalization performance. Abstract: With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L 2, 1 -norm named L 2, 1 -Extreme Learning Machine ( L 2, 1 -ELM) has been proposed. The method introduces L 2, 1 -norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.