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A Hybrid Feature Selection Method Based on Symmetrical Uncertainty and Support Vector Machine for High-Dimensional Data Classification

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

MicroRNA (miRNA) is a small, endogenous, and non-coding RNA that plays a critical regulatory role in various biological processes. Recently, researches based on microRNA expression profiles showed a new aspect of multiclass cancer classification. Due to the high dimensionality, however, classification of miRNA expression data contains several computational challenges. In this paper, we proposed a hybrid feature selection method for accurately classification of various cancer types based on miRNA expression data. Symmetrical uncertainty was employed as a filter part and support vector machine with best first search were used as a wrapper part. To validate the efficiency of the proposed method, we conducted several experiments on a real bead-based miRNA expression datasets and the results showed that our method can significantly improve the classification accuracy and outperformed the existing feature selection methods.

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Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2013R1A2A2A01068923) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1013) supervised by the IITP(Institute for Information & communication Technology Promotion).

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Authors and Affiliations

  1. Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea

    Yongjun Piao & Keun Ho Ryu

Authors
  1. Yongjun Piao
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  2. Keun Ho Ryu
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Corresponding author

Correspondence to Keun Ho Ryu .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Piao, Y., Ryu, K.H. (2017). A Hybrid Feature Selection Method Based on Symmetrical Uncertainty and Support Vector Machine for High-Dimensional Data Classification. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_67

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

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