1. Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis.
- Author
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Lee, Kevin, Man, Zhihong, Wang, Dianhui, and Cao, Zhenwei
- Subjects
CANCER diagnosis ,BIOINFORMATICS ,FINITE impulse response filters ,MACHINE learning ,TIME series analysis ,MICROARRAY technology ,ALGORITHMS ,PERFORMANCE evaluation - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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