NDLI: Neuron identification by classification tree and particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Wenwen Huang Nianli Liu
Copyright Year	2012
Description	Author affiliation: College of Arts and Science, Yangtze University, Jingzhou Hubei, China (Wenwen Huang) \|\| College of Accounting, Hubei University of Economics, Wuhan Hubei, China (Nianli Liu)
Abstract	In this paper, the adaptive method, which is on the basis of hierarchical classification tree and particle swarm optimzation(PSO), is proposed to classify different neurons in the international benchmark samples. Firstly, the similarity, corresponding to the maximum and the minimum of each feature, is calculated to denote the characteristic of the neuron, and then this paper designs the rules of classification tree and utilizes PSO algorithm to optimizate crucial parameters in the classification tree. To show high performance and the effectiveness of this proposed algorithm, the successful percentage of discerning the neuron can achieve to 98.04%.
Starting Page	3331
Ending Page	3334
File Size	318015
Page Count	4
File Format	PDF
ISBN	9781457714146
e-ISBN	9781457714153
DOI	10.1109/CECNet.2012.6201506
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Geometric Characteristics of Neuron Morphology Neurons Surface morphology Classification of Neurons Classification algorithms Mathematical model Particle swarm optimization Classification Tree Particle Swarm Optimization Classification tree analysis
Content Type	Text
Resource Type	Article

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