Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis |
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Authors: | Kevin Lee Zhihong Man Dianhui Wang Zhenwei Cao |
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Affiliation: | 1. Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia 2. Department of Computer Science and Computer Engineering, La Trobe University, Bundorra, VIC, 3086, Australia
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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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