Variable Selection Using Adaptive Band Clustering and Physarum Network
AbstractVariable 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
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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.
Chen H, Chen T, Zhang Z, Liu G. Variable Selection Using Adaptive Band Clustering and Physarum Network. Algorithms. 2017; 10(3):73.Chicago/Turabian Style
Chen, Huanyu; Chen, Tong; Zhang, Zhihao; Liu, Guangyuan. 2017. "Variable Selection Using Adaptive Band Clustering and Physarum Network." Algorithms 10, no. 3: 73.
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