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A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

The high dimensionality of data is a common problem in classification. In this work, a small number of significant features is investigated to classify data of two sample groups. Various feature selection and classification techniques are applied in a collection of four high-throughput DNA methylation microarray data sets. Using accuracy as a performance metric, the repeated 10-fold cross-validation strategy is implemented to evaluate the different proposed techniques. Combining the Signal to Noise Ratio (SNR) and Wilcoxon rank-sum test filter methods with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) as an embedded method has resulted in a perfect performance. In addition, the linear classifiers showed excellent results compared to others classifiers when applied to such data sets.

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Authors and Affiliations

  1. Faculty of Computers and Information, Department of Computer Science, Cairo University, Giza, Egypt

    Alhasan Alkuhlani, Mohammad Nassef & Ibrahim Farag

Authors
  1. Alhasan Alkuhlani
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  2. Mohammad Nassef
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  3. Ibrahim Farag
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Corresponding author

Correspondence to Alhasan Alkuhlani .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Alkuhlani, A., Nassef, M., Farag, I. (2017). A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_76

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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