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Improvements in the Large p, Small n Classification Issue

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Abstract

Classifying gene expression data is known to contain keys for solving the fundamental problems in cancer studies. However, this issue is a complex task because of the large p, small n issue on gene expression data analysis. In this paper, we propose the improvements in the large p, small n classification issue for the study of human cancer. First, a new enhancing sample size method with generative adversarial network is proposed to improve classification algorithms. Second, we suggest a classification approach with over-sampling technique using features extracted by deep convolutional neural network. Numerical test results on fifty very-high-dimensional and low-sample-size gene expression data datasets from the Kent Ridge Biomedical and Array Expression repositories illustrate that the proposed models are more accurate than state-of-the-art classifying models. In addition, we also have explored the performance of support vector machines, k nearest neighbors and random forests, which have improved when apply our approaches.

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Authors and Affiliations

  1. Faculty of Information Technology, An Giang University, Angiang, Vietnam

    Phuoc-Hai Huynh & Van Hoa Nguyen

  2. Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Phuoc-Hai Huynh & Van Hoa Nguyen

  3. College of Information Technology, Can Tho University, Cantho, Vietnam

    Thanh-Nghi Do

  4. UMI UMMISCO 209 (IRD/UPMC), Sorbonne University, Pierre and Marie Curie University, Paris 6, France

    Thanh-Nghi Do

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This article is part of the topical collection “Future Data and Security Engineering 2019” guest edited by Tran Khanh Dang.

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Huynh, PH., Nguyen, V.H. & Do, TN. Improvements in the Large p, Small n Classification Issue. SN COMPUT. SCI. 1, 207 (2020). https://doi.org/10.1007/s42979-020-00210-2

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