Fatma Yerlikaya Özkurt

Yerlikaya Özkurt, Fatma

Name Variants

Yerlikaya-Özkurt,F.
F., Yerlikaya Ozkurt
Y.,Fatma
Y., Fatma
Yerlikaya Özkurt,F.
Fatma Yerlikaya Özkurt
Yerlikaya Ozkurt,Fatma
Yerlikaya Ozkurt,F.
F., Yerlikaya Özkurt
Yerlikaya Ozkurt, Fatma
Fatma, Yerlikaya Özkurt
F.,Yerlikaya Özkurt
Y.Ö.Fatma
F.,Yerlikaya Ozkurt
Fatma, Yerlikaya Ozkurt
Y. Ö. Fatma
Y. O. Fatma
Yerlikaya Özkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-oezkurt, Fatma
Yerlikaya-Ozkurt,F.
Özkurt, Fatma Yerlikaya
Ozkurt, F. Yerlikaya

Job Title

Doktor Öğretim Üyesi

Email Address

fatma.yerlikaya@atilim.edu.tr

Main Affiliation

Industrial Engineering

Website

http

ORCID ID

0000-0002-4747-8461

Full item page

Scholarly Output

15

Articles

12

Citation Count

33

Supervised Theses

1 Scholarly Output Search Results

Now showing 1 - 10 of 15

Citation - WoS: 2
Citation - Scopus: 3
Cmars: a Powerful Predictive Data Mining Package in R
(Elsevier, 2023) Yerlikaya-oezkurt, Fatma; Yazici, Ceyda; Batmaz, Inci; Industrial Engineering
Conic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Quadratic Optimization (CQO). In this paper, the open-source R package, cmaRs, built to construct CMARS models for prediction and binary classification is presented with illustrative applications. Also, the CMARS algorithm is provided in both pseudo and R code. Note here that cmaRs package provides a good example for a challenging implementation of CQO based on MOSEK solver in R environment by linking R MOSEK through the package Rmosek.
Citation - WoS: 2
Citation - Scopus: 2
Estimation in the Partially Nonlinear Model by Continuous Optimization
(Taylor & Francis Ltd, 2021) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Tez, Mujgan; Industrial Engineering
A useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.
Citation - WoS: 15
Citation - Scopus: 15
A New Outlier Detection Method Based on Convex Optimization: Application To Diagnosis of Parkinson's Disease
(Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-Wilhelm; Department of Electrical & Electronics Engineering; Industrial Engineering
Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.
Citation - WoS: 8
Citation - Scopus: 7
New Computational Methods for Classification Problems in the Existence of Outliers Based on Conic Quadratic Optimization
(Taylor & Francis inc, 2020) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Industrial Engineering
Most of the statistical research involves classification which is a procedure utilized to establish prediction models to set apart and classify new observations in the dataset from every fields of science, technology, and economics. However, these models may give misclassification results when dataset contains outliers (extreme data points). Therefore, we dealt with outliers in classification problem: firstly, by combining robustness of mean-shift outlier model and then stability of Tikhonov regularization based on continuous optimization method called Conic Quadratic Programming. These new methodologies are performed on classification dataset within the existence of outliers, and the results are compared with parametric model by using well-known performance measures.
Citation - WoS: 1
Citation - Scopus: 1
A Novel Comparison of Shrinkage Methods Based on Multi Criteria Decision Making in Case of Multicollinearity
(American Institute of Mathematical Sciences, 2024) Kılıçoğlu,Ş.; Yerlikaya-Özkurt,F.; Industrial Engineering
Streszczenie. Data analysis is very important in many fields of science. The most preferred methods in data analysis is linear regression due to its simplicity to interpret and ease of application. One of the assumptions accepted while obtaining linear regression is that there is no correlation between the independent variables in the model which refers to absence of multicollinearity. As a result of multicollinearity, the variance of the parameter estimates will be high and this reduces the accuracy and reliability of the linear models. Shrinkage methods aim to handle the multicollinearity problem by minimizing the variance of the estimators in linear model. Ridge Regression, Lasso, and Elastic-Net methods are applied to different simulated data sets with different characteristics and also real world data sets. Based on performance results, the methods are compared according to multi-criteria decision making method named TOPSIS, and the order of preference is determined for each data set. © (2024), (American Institute of Mathematical Sciences). All rights reserved.
Citation - WoS: 7
Citation - Scopus: 8
Prediction of Potential Seismic Damage Using Classification and Regression Trees: a Case Study on Earthquake Damage Databases From Turkey
(Springer, 2020) Yerlikaya-Ozkurt, Fatma; Askan, Aysegul; Industrial Engineering
Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Severe are used, while five structural parameters are employed as damage identifiers. For validation, results of classification analyses are compared against observed damage states. Results in terms of well-known classification performance measures indicate that when the size of the database is larger, the correct classification rates are higher. Performance measures computed for Test data set indicate similar success to that of Train data set. The approach is found to be effective in classifying randomly selected damage data.
Citation - WoS: 0
Citation - Scopus: 0
Modeling of Kappa Factor Using Multivariate Adaptive Regression Splines: Application To the Western Türkiye Ground Motion Dataset
(Springer, 2024) Kurtulmus, Tevfik Ozgur; Yerlikaya-Ozkurt, Fatma; Askan, Aysegul; Industrial Engineering
The recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.
Citation - WoS: 2
A new approach to adaptive spline threshold autoregression by using Tikhonov regularization and continuous optimization
(Taru Publications, 2019) Yalaz, S.; Taylan, P.; Ozkurt, F. Yerlikaya; Industrial Engineering
In this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularization problem which was transformed into conic quadratic programming problem. The aim is to attain an optimum solution chosen in many solutions obtained by determining the bounds of the optimization problem using multiobjective optimization approach. Furthermore, in application part we used two different data set to compare performances of linear regression, adaptive spline threshold autoregression and conic adaptive spline threshold autoregression approaches.
Citation - WoS: 0
Citation - Scopus: 0
Spline Based Sparseness and Smoothness for Partially Nonlinear Model Via C-Fused Lasso
(American Institute of Mathematical Sciences, 2025) Taylan, P.; Yerlikaya-¨Ozkurt, F.; Tez, M.; Industrial Engineering
One of the most beneficial and widely used models for data analysis are partially nonlinear models (PNLRM), which consists of parametric and nonparametric components. Since the model includes the coefficients of both the parametric and nonparametric parts, the complexity of the model will be high and its interpretation will be very difficult. In this study, we propose a procedure that not only achieves sparseness, but also smoothness for PNLRM to obtain a simpler model that better explains the relationship between the response and covariates. Thus, the fused Lasso problem is taken into account where nonparametric components are expressed as a spline basis function, and then the Fused Lasso estimation problem is built and expressed in terms of conic quadratic programming. Applications are conducted to evaluate the performance of the proposed method by considering commonly utilized measures. Promising results are obtained, especially in the data with nonlinearly correlated variables. © (2025), (American Institute of Mathematical Sciences). All rights reserved.
Konut Piyasalarında Makroekonomik Faktörlerin Etkisi: Abd Örneği
(2021) Yılmaz, Bilgi; Kestel, A. Sevtap Selçuk; Özkurt, Fatma Yerlikaya; Industrial Engineering
Bu makale makroekonomik, finansal ve emtia piyasaları göstergelerinin konut piyasaları üzerindeki etkisini analiz etmektedir. Genelleştirilmiş Doğrusal Modeller (GDM) ve Çok Değişkenli Uyarlanabilir Regresyon Eğrileri (ÇDRE) tarafından üretilen modellerin yeterliliğini konut piyasası eğilimini tahmin etmek için bağımsız ölçüm yöntemlerine göre karşılaştırıyoruz. Araştırmalarımıza göre bu modeller ilk kez makroekonomik göstergelerin konut piyasaları üzerindeki etkisini ve konut piyasalarındaki eğilimine yönelik tahmini belirlemekte kullanılmaktadır. Ampirik çalışmalar, ABD konut piyasalarına odaklanmakta ve önerilen modellerin gösterimi Ocak 1999-Haziran 2018 periyodu arasında gözlemlenen aylık S\\&P/Case-Shiller Ulusal Konut Fiyat İndeksine ve ABD macroeconomic faktörlerine uygulanmaktadır. Bu çalışma makro ekonomik göstergeler ve konut piyasaları arasındaki etkileşimi vurgulayarak ve konut piyasalarının mekanizmasını analiz ederek literatüre katkıda bulunmaktadır. Bulguları, konut fiyat eğilimlerinin daha doğru bir şekilde tahmin edildiğini ve bu modellerin açıklayıcı değişkenlerin ortak etkisini yakaladığını göstermektedir. Ayrıca, ÇDRE yönteminin tahmin ve geleceğe yönelik tahmin gücüne kıyasla GDM'den daha iyi performans gösterdiğini ortaya koymuştur.

Yerlikaya Özkurt, Fatma

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Scholarly Output

15

Articles

12

Citation Count

33

Supervised Theses

1

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