Rule extraction from spatial data using local learning techniques

We are now in the fourth decade where techniques such as fuzzy systems, statistics, neural networks and machine learning have all been developed and more recently applied for the purpose of spatial data mining. However these methods act as global learning models and subsequently may not be able to learn the subtle nature of these types of data sets. Local learning models such as the Support Vector Machine (SVM) and a more recent method such as that proposed by (Gilardi 2002) address the problem of global versus local learning but fail to offer many solutions as to what underlying patterns may exist within the data set in order to better understand the data set. In this paper we propose the Evolving Fuzzy Neural Network (EFuNN) as a model for a local learning mechanism for the purpose of predicting rainfall within a region of Switzerland and also use this model to generate rules and then to visualise these rules that may help to describe any patterns that may exist within the data set.PublishedNon Peer ReviewedAmari, S. & Kasabov, N. (1997). Brain-like computing and intelligent information systems. first edn. Springer Verlag. Carpenter, G. & Grossberg, S. (1991). Pattern Recognition By Self Organizing Neural Networks. first edn. MIT Press: Cambridge, MA. Chawla, S., Shekhar, S., Wu, W. & Ozesmi, U. (2001). “Modeling Spatial Dependencies for Mining Geospatial Data: An Introduction” In H. J. Miller & J. Han (eds), Geographic Data Mining and Knowledge Discovery. Taylor and Francis. Cortes, C. & Vapnik, V. (1995). “Support-Vector Networks” Machine Learning. 20(3): 273–297. Dubois, G., Malzekski, J. & DeCort, M. (1998). “Spatial Interpolation Comparison” Journal of Geographic Information and Decision Analysis. 2(2). Gilardi, N. (2002). “Local Machine Learning Models for Spatial Data Analysis” Geographical Information and Decision Analysis. 4(1): 11–28. Kasabov, N. (1998a). “ECOS: A Framework For Evolving Connectionist Systems and the ECO Learning Paradigm” Proceedings of ICONIP’98, Kitakyushu, Oct 1998. Ohmsha, Ltd: Tokyo, Japan. pp. 1232–1236. Kasabov, N. (1998b). “Evolving Fuzzy Neural Networks - Algorithms, Applications and Biological Motivation” Proceedings of Iizuka’98, Iizuka, Japan. World Scientific. pp. 271–274. Kasabov, N. (1998c). “Fuzzy Neural Networks, Rule Extraction and Fuzzy Synergistic Reasoning Systems” Research and Information Systems. 8: 45–59. Kasabov, N. (1999a). Evolving Connectionist And Fuzzy Connectionist System For On-Line Decision Making And Control. Vol. Soft Computing in Engineering Design and Manufacturing Springer-Verlag. Kasabov, N. (1999b). Evolving Connectionist and Fuzzy-Connectionist Systems: Theory and Applications for Adaptive, On-line Intelligent Systems. Vol. Neuro-fuzzy Tools and Techniques for Intelligent Systems, N Kasabov and R Kozma (eds) first edn. Springer-Verlag. Kasabov, N. (2001). “On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks” Neurocomputing. 41(1–4): 25–45. Kasabov, N., Kim, J. S., Watts, M. & Gray, A. (1996). “FuNN/2- A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition” Information Sciences - Applications. 101(3-4): 155–175. Kasabov, N. & Woodford, B. (1999). “Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems” Proceedings of the 1999 IEEE Fuzzy Systems Conference. Vol. 3 The IEEE Kyunghee Printing Co. pp. 1406–1411. Kohonen, T. (1990). “The Self-Organizing Map” Proceedings of the IEEE. 78(9): 1464–1497. Kohonen, T. (1997). Self-Organizing Maps. second edn. Springer-Verlag. Ma, J. & Ahalt, S. (2001). “OSU SVM Classifier Matlab Toolbox (ver 2.00)”. Mitchell, M. T. (1997). Machine Learning. MacGraw-Hill. Rumelhart, D. E., Hinton, G. E. &Williams, R. J. (1986). Parallel Distributed Processing, Vols 1 and 2. The MIT Press: Cambridge, MA. Sammon, J. W. (1969). “A Nonlinear Mapping for Data Structure Analysis” IEEE Transactions on Computers. 18: 401–409. Setiono, R. & Liu, H. (1997). “NeuroLinear: from neural networks to oblique decision rules” Neurocomputing. 17(1): 1–24. Smola, A. J. & Sch¨olkopf, B. (1998). “A Tutorial on Support Vector Regression” Technical Report NC2-TR- 1998-030. NeuroCOLT 2. Zadeh, L. (1965). “Fuzzy Sets” Information and Control. 8: 338–353