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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 626975, 9 pages
http://dx.doi.org/10.1155/2015/626975
Research Article

Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

1Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Hunan Agricultural University, Changsha, Hunan 410128, China
2Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha, Hunan 410128, China
3College of Information Science and Technology, Hunan Agricultural University, Changsha, Hunan 410128, China

Received 24 January 2015; Revised 14 March 2015; Accepted 15 March 2015

Academic Editor: Michele Migliore

Copyright © 2015 Congwei Sun et al. 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.

Abstract

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained.