A Probability Neural Network for Continuous and Categorical Data
| Authors: | Yu Hongnian, Staffordshire University, United Kingdom
Cang Shuang, University of Wales, Aberystwyth, United Kingdom |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Intelligent Modelling and Identification II |
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| Keywords: | Neural Networks, Probability Density Function (PDF), Classification, Pattern Recognition, Mixture Models, Expectation Maximisation (EM) algorithm |
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Abstract
This paper presents a novel Probability Neural Network (PNN) which can classify the data for both continuous and categorical input data types. The proposed PNN has two advantages comparing with the conventional algorithms such as the MLP Neural Network. One is that the PNN can produce better results comparing with the MLP Neural Network. Another advantage is that the PNN does not need the cross validation data set and does not produce the over training like the MLP neural network does. These have been proven in our experimental study. The proposed PNN can also be used to perform the unsupervised cluster analysis. The superiority of PNN in comparing the MLP neural network is demonstrated by applying them to a real-life data set, the Trauma data set which includes both continuous and categorical variables.
