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International Conference on Future Data and Security Engineering

FDSE 2019: Future Data and Security Engineering pp 255–273Cite as

A Combined Enhancing and Feature Extraction Algorithm to Improve Learning Accuracy for Gene Expression Classification

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

Abstract

In recent years, gene expression data combined with machine learning methods revolutionized cancer classification which had been based solely on morphological appearance. However, the characteristics of gene expression data have very-high-dimensional and small-sample-size which lead to over-fitting of classification algorithms. We propose a novel gene expression classification model of multiple classifying algorithms with synthetic minority oversampling technique (SMOTE) using features extracted by deep convolutional neural network (DCNN). In our approach, the DCNN extracts latent features of gene expression data, then the SMOTE algorithm generates new data from the features of DCNN was implemented. These models are used in conjunction with classifiers that efficiently classify gene expression data. Numerical test results on fifty very-high-dimensional and small-sample-size gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories illustrate that the proposed algorithm is more accurate than state-of-the-art classifying models and improve the accuracy of classifiers including non-linear support vector machines (SVM), linear SVM, k nearest neighbors and random forests.

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Author information

Authors and Affiliations

  1. Information Technology Faculty, An Giang University, Angiang, Viet Nam

    Phuoc-Hai Huynh & Van-Hoa Nguyen

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

    Thanh-Nghi Do

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

    Thanh-Nghi Do

Authors
  1. Phuoc-Hai Huynh
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  2. Van-Hoa Nguyen
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  3. Thanh-Nghi Do
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Corresponding author

Correspondence to Phuoc-Hai Huynh .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Huynh, PH., Nguyen, VH., Do, TN. (2019). A Combined Enhancing and Feature Extraction Algorithm to Improve Learning Accuracy for Gene Expression Classification. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_17

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  • Publisher Name: Springer, Cham

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