Krzysztof Trawiński

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

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.

On employing fuzzy modeling algorithms for the valuation of residential premises

In this paper, we investigate fuzzy modeling techniques for predicting the prices of residential premises, based on some main drivers such as usable area of premises, age of a building, number of rooms in a flat, floor on which a flat is located, number of storeys in a building as well as the distance from the city center. Our proposed modeling techniques rely on two aspects: the first one (called SparseFIS) is a batch off-line modeling method and tries to out-sparse an initial dense rule population by optimizing the rule weights within an iterative optimization procedure subject to constrain the number of important rules; the second one (called FLEXFIS) is a single-pass incremental method which is able to build up fuzzy models in an on-line sample-wise learning context. As such, it is able to adapt former generated prediction models with new data recordings on demand and also to cope with on-line data streams. The final obtained fuzzy models provide some interpretable insight into the relations between the various features and residential prices in form of linguistically readable rules (IF-THEN conditions). Both methods will be compared with a state-of-the-art premise estimation method usually conducted by many experts and exploiting heuristic concepts such as sliding time window, nearest neighbors and averaging. The comparison is based on a two real-world data set including prices for residential premises within the years 1998-2008.

Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal

The experiments, aimed to compare three methods to create ensemble models implemented in a popular data mining system called WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, linear regression, and support vector machine were used to construct ensemble models. All algorithms were employed to real-world datasets derived from the cadastral system and the registry of real estate transactions. Nonparametric Wilcoxon signed-rank tests to evaluate the differences between ensembles and original models were conducted. The results obtained show there is no single algorithm which produces the best ensembles and it is worth to seek an optimal hybrid multi-model solution.

Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals

The study reported was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including two evolutionary fuzzy systems, decision trees for regression, and neural network, were used in the experiments. The results showed that some bagging ensembles ensured higher prediction accuracy than single models.

A Multi-agent System to Assist with Real Estate Appraisals Using Bagging Ensembles

The multi-agent system for real estate appraisals MAREA was extended to include aggregating agents, which could create ensemble models applying the bagging approach, was presented in the paper. The major part of the study was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including linear regression, decision trees for regression, support vector machines, and artificial neural network of MLP type, were used in the experiments. The results showed that bagging ensembles ensured higher prediction accuracy than single models.

Comparison of mamdani and TSK fuzzy models for real estate appraisal

Two fuzzy models for real estate appraisal, i.e. Mamdani-type and Takagi-Sugeno-Kang-type have been built with the aid of experts. Both models comprised 7 input variables referring to main attributes of a property being appraised. In order to determine the rule bases for both models an evolutionary algorithm has been applied. The experiments revealed that models assured acceptable estimations of property values.

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.

Exploration of Soft Computing Models for the Valuation of Residential Premises Using the KEEL Tool

The experiments aimed to compare data driven soft computing models for the valuation of residential premises were conducted using the KEEL tool (Knowledge Extraction based on Evolutionary Learning). Twelve regression algorithms were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. The 5x2-fold cross validation and nonparametric statistical tests were applied. The results proved the usefulness of the tool to carry out laborious investigation in a relatively short time. Further research is needed to determine to what extent data coming from such sources allow to build the real estate valuation models.

Concept of a Multi-Agent System for Assisting in Real Estate Appraisals

The general architecture of a multi-agent system for real estate appraisal (MAREA) is presented in the paper. The appraisal data warehouse is filled with data drawn from source cadastral databases. Data driven appraisal models are created using different machine learning algorithms. Architecture of the MAREA system in the JADE platform was also proposed. Several experiments aimed to assess the usefulness of different machine learning algorithms for the system were conducted using the KEEL tool.

Comparison of Evolving Fuzzy Systems with anäEnsemble Approach to Predict from a Data Stream

An approach to apply ensembles of regression models, built over the chunks of a data stream, to aid in residential premises valuation was proposed. The approach consists in incremental expanding an ensemble by systematically generated models in the course of time. The output of aged component models produced for current data is updated according to a trend function reflecting the changes of premises prices since the moment of individual model generation. The method employing general linear model, multiple layer perceptron, and radial basis function networks was empirically compared with evolving fuzzy systems designed for incremental learning from data streams.The results showed thatevolving fuzzy systems and general linear models outperformed the ensembles built using artificial neural networks.