Comparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal

Król, Dariusz; Lasota, Tadeusz; Trawiński, Bogdan; Trawiński, Krzysztof

doi:10.1007/978-3-540-74829-8_123

Cited by 18 publications

(6 citation statements)

References 5 publications

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“…Several experiments were conducted on the models [3], [4], [5] using Matlab package. They were aimed at evolutionary learning of rule bases and/or tuning of membership functions.…”

Section: Figure 1 Components Of a Type-2 Flsmentioning

confidence: 99%

An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals

Sygnowski

Trawiński

Zgrzywa

2008

2008 1st International Conference on Information Technology

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A Takagi-Sugeno-Kang type-1 fuzzy system to assist with real estate appraisals was developed with the aid of experts and comprised input variables relating to main attributes of a property being appraised. The rule bases of TSK zero-order fuzzy systems were generated using evolutionary algorithms. In order to reflect the uncertainty of available data, the type-1 fuzzy model was extended to the type-2 one using a singleton function as the second degree membership function. The comparative study of both models was carried out. The experiments were conducted using actual data.

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“…Several experiments were conducted on the models [3], [4], [5] using Matlab package. They were aimed at evolutionary learning of rule bases and/or tuning of membership functions.…”

Section: Figure 1 Components Of a Type-2 Flsmentioning

confidence: 99%

An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals

Sygnowski

Trawiński

Zgrzywa

2008

2008 1st International Conference on Information Technology

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show abstract

“…genetic fuzzy systems and artificial neural networks as both single models [7] and ensembles built using various resampling techniques [8], [9], [10], [11], [12], [13]. An especially good performance revealed evolving fuzzy models applied to cadastral data [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Neural Network Ensemble Approach to Predict from a Data Stream

Telec¹,

Trawiński²,

Lasota³

et al. 2014

Computational Collective Intelligence. Technologies and Applications

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Abstract.We have recently worked out a method for building reliable predictive models from a data stream of real estate transactions which applies the ensembles of genetic fuzzy systems and neural networks. The method consists in building models over the chunks of a data stream determined by a sliding time window and enlarging gradually an ensemble by models generated in the course of time. The aged models are utilized to compose ensembles and their output is updated with trend functions reflecting the changes of prices in the market. In the paper we present the next series of extensive experiments to evaluate our method with the ensembles of artificial neural networks. We examine the impact of the number of aged models used to compose an ensemble on the accuracy and the influence of the degree of polynomial trend functions employed to modify the results on the performance of neural network ensembles. The experimental results were analysed using statistical approach embracing nonparametric tests followed by post-hoc procedures designed for multiple N×N comparisons.

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“…Several Mamdani and Takagi-Sugeno-Kang-type (TSK) fuzzy models to assist with real estate appraisal were developed and evaluated [17,18] by the authors so far. The experiments conducted using MATLAB software package were rather time consuming so that much effort was devoted to find more time effective solutions comprising testing the fuzzy models with reduced number of input variables, different parameters of the rule base, fuzzy membership functions as well as the evolutionary optimization process [17,18,21,22,28].…”

Section: Introductionmentioning

confidence: 99%

“…The experiments conducted using MATLAB software package were rather time consuming so that much effort was devoted to find more time effective solutions comprising testing the fuzzy models with reduced number of input variables, different parameters of the rule base, fuzzy membership functions as well as the evolutionary optimization process [17,18,21,22,28]. The goal of all fuzzy models was to predict the prices of land real estates.…”

Section: Introductionmentioning

confidence: 99%

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

Lasota

Mazurkiewicz

Trawiński

et al. 2010

HIS

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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.

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Comparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal

Cited by 18 publications

References 5 publications

An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals

An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals

Evaluation of Neural Network Ensemble Approach to Predict from a Data Stream

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

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