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Brendon J Woodford, Department of Information Science


Rule extraction and visualisation from spatial data using a entropy-based fuzzy neural network


Archway 2 - 1:00 pm, Friday 14 August


As we are now in the fourth decade where techniques such as fuzzy systems, statistics, neural networks, and machine learning techniques have all been developed and more recently applied for the purpose of spatial data mining, their deficiencies have also been identified and have begun to be addressed.

One particular problem with these methods is that they mostly act as global learning models and subsequently may not be able to learn the subtle nature of these types of data sets. An alternative is to apply local-learning models such as the Support Vector Machine (SVM) and a more recent method such as that proposed by Gilardi (2002) to address the problem of global learning versus local-learning.

However, these methods fail to offer many solutions as to what underlying patters may exist within the data set in order to better understand it. In this work we propose a modified version of the Evolving Fuzzy Neural Network (EFuNN) as a model for local-learning. We apply this model for the purpose of predicting rainfall within a region of Switzerland and also use this model to generate rules which are then visualised as a means of identifying any patterns which may exist within the data set.

Last modified: Wednesday, 12-Aug-2009 10:22:51 NZST

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