Paper Info
Title
L-2,L-1-Extreme Learning Machine: An Efficient Robust Classifier For Tumor Classification
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,L-1-norm named L-2,L-1-Extreme Learning Machine (L-2,L-1-ELM) has been proposed. The method introduces L-2,L-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.
Year
DOI
Venue
2020
10.1016/j.compbiolchem.2020.107368
COMPUTATIONAL BIOLOGY AND CHEMISTRY
Keywords
DocType
Volume
Extreme Learning Machine, L-2,L-1-norm, Robust, Single-cell RNA Sequencing, Supervised Learning
Journal
89
ISSN
Citations 
PageRank 
1476-9271
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Liang-Rui Ren111.70
Gao Ying-Lian22918.73
Liu Jin-Xing34016.11
Rong Zhu46224.70
Xiang-Zhen Kong500.34