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Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

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Abstract

Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grants 61772176, 61402153, 61672332, 61370169, and 61472042), the China Postdoctoral Science Foundation (Grant 2016M602247), the Plan for Scientific Innovation Talent of Henan Province (Grant 184100510003), the Key Project of Science and Technology Department of Henan Province (Grants 182102210362), the Young Scholar Program of Henan Province (Grant 2017GGJS041), the Key Scientific and Technological Project of Xinxiang City (Grant CXGG17002), and the Ph.D. Research Foundation of Henan Normal University (Grants qd15132, qd15129).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Lin Sun, Xiao-Yu Zhang, Jiu-Cheng Xu & Shi-Guang Zhang

  2. Post-doctoral Mobile Station of Biology, College of Life Science, Henan Normal University, Xinxiang, 453007, China

    Lin Sun

  3. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, China

    Yu-Hua Qian

  4. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Yun Tian

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  1. Lin Sun
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Correspondence to Jiu-Cheng Xu.

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Sun, L., Zhang, XY., Qian, YH. et al. Joint neighborhood entropy-based gene selection method with fisher score for tumor classification. Appl Intell 49, 1245–1259 (2019). https://doi.org/10.1007/s10489-018-1320-1

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