Investigation of evolving fuzzy systems methods FLEXFIS and eTS on predicting residential prices

Authors:
Bogdan Trawiński;Krzysztof Trawiński;Edwin Lughofer;Tadeusz Lasota
Affiliations:
Wrocław University of Technology, Institute of Informatics, Wybrzeże, Wrocław, Poland;European Centre for Soft Computing, Edificio Científico-Tecnológico, Mieres, Spain;Johannes Kepler University Linz, Department of Knowledge-based Mathematical Systems, Linz, Austria;Wrocław University of Environmental and Life Sciences, Dept. of Spatial Management Ul. Norwida, Wrocław, Poland
Venue:
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Year:
2011

Citing 11
Cited 0

Fuzzy Systems as Universal Approximators

IEEE Transactions on Computers
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
ANN+GIS: An automated system for property valuation

Neurocomputing
Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal

International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Determinants of house prices in Turkey: Hedonic regression versus artificial neural network

Expert Systems with Applications: An International Journal
Looking for a good fuzzy system interpretability index: An experimental approach

International Journal of Approximate Reasoning
The mass appraisal of the real estate by computational intelligence

Applied Soft Computing
Investigation of bagging ensembles of genetic neural networks and fuzzy systems for real estate appraisal

ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Fuzzy systems with defuzzification are universal approximators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (via fuzzy sets and fuzzy rule bases). The FLEXFIS and eTS approaches are evolving fuzzy models used to compare with an expert-based property valuating method as well as with a classic genetic fuzzy system. We use a real-world dataset taken from a cadastral system for that comparison.