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Algorithms 2017, 10(3), 73; doi:10.3390/a10030073

Variable Selection Using Adaptive Band Clustering and Physarum Network

1
Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China
2
School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Received: 19 April 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 27 June 2017
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Abstract

Variable selection is a key step for eliminating redundant information in spectroscopy. Among various variable selection methods, the physarum network (PN) is a newly-introduced and efficient one. However, the whole spectrum has to be equally divided into sub-spectral bands in PN. These division criteria limit the selecting ability and prediction performance. In this paper, we transform the spectrum division problem into a clustering problem and solve the problem by using an affinity propagation (AP) algorithm, an adaptive clustering method, to find the optimized number of sub-spectral bands and the number of wavelengths in each sub-spectral band. Experimental results show that combining AP and PN together can achieve similar prediction accuracy with much less wavelength than what PN alone can achieve. View Full-Text
Keywords: affinity propagation; physarum network; variable selection; wavelength selection; real-time spectroscopy; on-line analysis affinity propagation; physarum network; variable selection; wavelength selection; real-time spectroscopy; on-line analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, H.; Chen, T.; Zhang, Z.; Liu, G. Variable Selection Using Adaptive Band Clustering and Physarum Network. Algorithms 2017, 10, 73.

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