Classification of escherichia coli bacteria by artificial neural networks

Author

Avci, Mutlu ; Yildirim, T.

Author_Institution

Electron. & Commun. Eng. Dept., Yildiz Tech. Univ., Istanbul, Turkey

Volume

3

fYear

2002

fDate

2002

Firstpage

13

Abstract

Through this paper, four different neural network structures which are: multilayer perceptron, radial basis function, general regression neural network and probabilistic neural network are applied to the escherichia coli bacteria benchmark and the most efficient neural network architecture for this data has been obtained. Better classification accuracy than the reference work using the ad hoc structured probability model was achieved by a probabilistic neural network.

Keywords

biology computing; classification; microorganisms; multilayer perceptrons; neural net architecture; probability; radial basis function networks; ad hoc structured probability model; backpropagation; escherichia coli bacteria classification; general regression neural network; multilayer perceptron; neural network architecture; probabilistic neural network; radial basis function neural net; Artificial neural networks; Bayesian methods; Benchmark testing; Biomembranes; Classification tree analysis; Decision trees; Microorganisms; Neural networks; Telephony; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2002. Proceedings. 2002 First International IEEE Symposium

Print_ISBN

0-7803-7134-8

Type

conf

DOI

10.1109/IS.2002.1042578

Filename

1042578