Ayşen Apaydin

Comparison of multi-objective algorithms applied to feature selection

The feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed.

Gamma distribution approach in chance-constrained stochastic programming model

In this article, a method is developed to transform the chance-constrained programming problem into a deterministic problem. We have considered a chance-constrained programming problem under the assumption that the random variables aij are independent with Gamma distributions. This new method uses estimation of the distance between distribution of sum of these independent random variables having Gamma distribution and normal distribution, probabilistic constraint obtained via Essen inequality has been made deterministic using the approach suggested by Polya. The model studied on in practice stage has been solved under the assumption of both Gamma and normal distributions and the obtained results have been compared.

Hybrid fuzzy support vector regression analysis

Support Vector Machines (SVM) has recently received a great deal of attention in regression and classification problems for crisp data. However, the fuzzy structure of the system should be considered if available information is uncertain or imprecise. In this paper, a new approach called Hybrid Fuzzy Support Vector Regression (HF-SVR) is introduced for the linear and non-linear fuzzy regression modeling. According to the proposed algorithm, parameter estimates are obtained from the solutions of two optimization problems by using the basic idea underlying SVM and the least squares principle. Furthermore, different learning machines for non-linear fuzzy regression can be constructed according to the selection of kernel function. In order to compare HF-SVR with the previously published fuzzy support vector regression methods, the data sets used in these papers are adopted. Based on the findings obtained from the numerical applications, it is determined that proposed method gives remarkable results according to the measure of mean square error.

A New Spatial Algorithm Based on Adaptive Fuzzy Neural Network for Prediction of Crustal Motion Velocities in Earthquake Research

In earthquake studies, different methods are used in modeling of the crustal motions. In case of obscurity data structure, different approaches are needed in solving motion problems. In this paper, a new spatial algorithm has been developed which is based on adaptive fuzzy neural network (AFNN) approach for the prediction of the crustal motion velocities. In order to find the fuzzy class numbers regarding the network model formed by the fuzzification of the studied area, subtractive clustering algorithm is used. In determining the membership function, utilization of the variogram function which models the relationship that depends on distance among spatial data is proposed. The Marmara Region, Turkey, is used as the case for this study. In order to evaluate the performance of the approach, the kriging method is also utilized in the prediction and the results obtained from both methods are compared based on the mean-square-error criteria. It is observed that the AFNN approach yields results which are as effective as those of kriging. Consequently, it is shown that the AFNN approach will contribute to earthquake studies.

Bulanık Çok Yanıtlı Deneyler İçin Bulanık Pareto Çözüm Kümesinin Bulanık İlişkiye Dayalı Sınıflandırma Yaklaşımı İle Değerlendirilmesi

The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multiresponse experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II) are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.

An Approach of Adaptive Network Based Fuzzy Inference System to Risk Classification in Life Insurance

In this paper, we propose ANFIS based system modeling for classifying risks in life insurance. We differentiate policyholders on the basis of their cardiovascular risk characteristics and estimate risk loading ratio to obtain gross premiums paid by the insured. In this context, an algorithm which expresses the relation between the dependent and independent variables by more than one model is proposed to use. Estimated values are obtained by using this algorithm, based on ANFIS. In order to show the performance evaluation of the proposed method, the results are compared with the results obtained from the Least Square Method (LSM).