Comparison of data driven models for the valuation of residential premises using KEEL

The experiments aimed to compare data driven models for the valuation of residential premises were conducted using KEEL (Knowledge Extraction based on Evolutionary Learning) system. Twelve different regression algorithms were applied to an actual data set derived from the cadastral system and the registry of real estate transactions. The 10-fold cross validation and statistical tests were applied. The lowest values of MSE provided models constructed and optimized by means of support vector machine, artificial neural network, decision trees for regression and quadratic regression, however differences between them were not statistically significant. Worse performance revealed algorithms employing evolutionary fuzzy rule learning. The experiments confirmed the usefulness of KEEL as a powerful tool with its numerous evolutionary algorithms together with classical learning approaches to carry out laborious investigation on a practical problem in a relatively short time.

[1]  Sarabjot Singh Anand,et al.  The application of intelligent hybrid techniques for the mass appraisal of residential properties , 1999 .

[2]  Bogdan Trawinski,et al.  Evolutionary generation of rule base in TSK fuzzy model for real estate appraisal , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.

[3]  Peter J. Wyatt,et al.  The Development of a GIS-Based Property Information System for Real Estate Valuation , 1997, Int. J. Geogr. Inf. Sci..

[4]  Carlo Bagnoli,et al.  The Theory of Fuzzy Logic and its Application to Real Estate Valuation , 1998 .

[5]  Alistair Munro,et al.  Evolving fuzzy rule based controllers using genetic algorithms , 1996, Fuzzy Sets Syst..

[6]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[7]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[8]  Dariusz Król,et al.  Comparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal , 2007, KES.

[9]  Timothy H. Greer,et al.  An appraisal tool for the 21st Century: Automated Valuation Models. , 2001 .

[10]  María José del Jesús,et al.  KEEL: A data mining software tool integrating genetic fuzzy systems , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.

[11]  Francisco Herrera,et al.  COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[12]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[13]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[14]  Piero P. Bonissone,et al.  Financial applications of fuzzy case-based reasoning to residential property valuation , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[15]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[16]  Marco Aurélio Stumpf González,et al.  Mass Appraisal With Genetic Fuzzy Rule-Based Systems , 2003 .

[17]  Bogdan Trawiński,et al.  Evolutionary Optimization of TSK Fuzzy Model to Assist with Real Estate Appraisals , 2008 .

[18]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[19]  Francisco Herrera,et al.  Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..

[20]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[21]  Margarita M. Lenk,et al.  An Exploration of Neural Networks and Its Application to Real Estate Valuation , 1995 .

[22]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[23]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[24]  Dariusz Król,et al.  Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal , 2008, Int. J. Hybrid Intell. Syst..

[25]  B. Trawinski,et al.  An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals , 2008, 2008 1st International Conference on Information Technology.

[26]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[27]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[28]  Nghiep Nguyen,et al.  Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks , 2001 .

[29]  Francisco Herrera,et al.  Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models , 2003, Modelling with Words.

[30]  Bogdan Trawinski,et al.  An Attempt to Use the KEEL Tool to Evaluate Fuzzy Models for Real Estate Appraisal , 2008, New Trends in Multimedia and Network Information Systems.

[31]  Jacek Mazurkiewicz,et al.  Investigation of Fuzzy Models for the Valuation of Residential Premises Using the KEEL Tool , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[32]  J. Rustagi.  Optimization Techniques in Statistics , 1994 .

[33]  Woubishet Zewdu Taffese,et al.  Case-based reasoning and neural networks for real state valuation , 2007 .

[34]  Dariusz Król,et al.  Fuzzy System Model to Assist with Real Estate Appraisals , 2007, IEA/AIE.