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Neural Computing and Applications

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01.03.2013 | Extreme Learning Machine's Theory & Application

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

verfasst von: Kevin Lee, Zhihong Man, Dianhui Wang, Zhenwei Cao

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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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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Titel: Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis
verfasst von: Kevin Lee
Zhihong Man
Dianhui Wang
Zhenwei Cao
Publikationsdatum: 01.03.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0847-z

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