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

Abstract

Takagi-Sugeno-Kang fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms. Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Moreover two model variants with three and five triangular and trapezoidal membership functions describing each input variable were tested. Several TSK fuzzy models comprising different number of input variables were evaluated using the MATLAB. The evolutionary algorithms were based on Pittsburgh approach with the real coded chromosomes of constant length comprising whole rule base or both the rule base and all parameters of all membership functions. The experiments were conducted using training and testing sets prepared on the basis of actual 150 sales transactions made in one of Polish cities and located in a residential section. The results obtained were not decisive and further research in this area is needed.

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

“…So far, the authors of the present paper have investigated several methods to construct regression models: evolutionary fuzzy systems, neural networks, decision trees, and statistical algorithms using MATLAB, KEEL, and WEKA data mining systems (Graczyk et al, 2009;Krzystanek et al, 2009;Król et al, 2008;Lasota et al, 2010), on the basis of a real-world case of real estate appraisals.…”

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

“…For example, Król et al [5] compared two different approaches based on evolutionary algorithms to assist with real estate appraisals. The experiments were conducted using training and testing sets prepared on the basis of 150 actual sale transactions made in a residential section of a Polish city.…”

A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

“…electroencephalogram (EEG) classification [1], diagnosis of industrial turbomachines [2], facial gender recognition [3], real estate appraisals [4], as well as awareness modeling in disaster management [5]. In this work, we focus on a neural-fuzzy hybrid model, i.e., the fuzzy ARTMAP (FAM) [6] network, for tackling pattern classification problems, and particularly in the medical domain.…”