Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree

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Author(s): Xu-Qin Li (University of Warwick, UK), Evor L. Hines (University of Warwick, UK), Mark S. Leeson (University of Warwick, UK)and D. D. Iliescu (University of Warwick, UK)
Copyright: 2011
Pages: 17
Source title: Intelligent Systems for Machine Olfaction: Tools and Methodologies
Source Author(s)/Editor(s): Evor L. Hines (University of Warwick, UK)and Mark S. Leeson (University of Warwick, UK)
DOI: 10.4018/978-1-61520-915-6.ch011

Keywords: Artificial Intelligence / Biologically-Inspired Computing / Engineering Science Reference / Medicine & Healthcare

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Abstract

Eye bacteria are vital to the diagnosis of eye disease, which makes the classification of such bacteria necessary and important. This chapter aims to classify different kinds of eye bacteria after the data were collected by an Electronic Nose. First the Multi-layer perceptron (MLP) and decision tree (DT) were introduced as the algorithm and the base classifiers. After that, the bagging technique was introduced to both algorithms and showed that the accuracy of the MLP had been significantly improved. Moreover, bagging to the DT not only reduced the misclassification rate, but enabled DT to select the most important features, and thus, decreased the dimension of the data facilitating an enhanced training and testing process.

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Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree

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