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Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis

  • Extreme Learning Machine's Theory & Application
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Abstract

In this paper, the classification of the two binary bioinformatics datasets, leukemia and colon tumor, is further studied by using the recently developed neural network-based finite impulse response extreme learning machine (FIR-ELM). It is seen that a time series analysis of the microarray samples is first performed to determine the filtering properties of the hidden layer of the neural classifier with FIR-ELM for feature identification. The linear separability of the data patterns in the microarray datasets is then studied. For improving the robustness of the neural classifier against noise and errors, a frequency domain gene feature selection algorithm is also proposed. It is shown in the simulation results that the FIR-ELM algorithm has an excellent performance for the classification of bioinformatics data in comparison with many existing classification algorithms.

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Authors and Affiliations

  1. Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Kevin Lee, Zhihong Man & Zhenwei Cao

  2. Department of Computer Science and Computer Engineering, La Trobe University, Bundorra, VIC, 3086, Australia

    Dianhui Wang

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  1. Kevin Lee
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  2. Zhihong Man
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  3. Dianhui Wang
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  4. Zhenwei Cao
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Correspondence to Kevin Lee.

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Lee, K., Man, Z., Wang, D. et al. Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis. Neural Comput & Applic 22, 457–468 (2013). https://doi.org/10.1007/s00521-012-0847-z

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  • DOI: https://doi.org/10.1007/s00521-012-0847-z

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