Browsing by Author "Taylan, Pakize"

Now showing 1 - 4 of 4

Citation - WoS: 2
Citation - Scopus: 2
Enhancing Classification Modeling Through Feature Selection and Smoothness: a Conic-Fused Lasso Approach Integrated With Mean Shift Outlier Modelling
(Amer inst Mathematical Sciences-aims, 2025) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize
Outlier detection and variable selection are among main objectives of statistical analysis. In our study, we address the outlier problem for classification by using the Mean Shift Outlier Model (CLMSOM). Since the MSOM has more coefficients than the linear regression model, the complexity of the model MSOM is high. Therefore, we consider feature selection for MSOM by using fused Lasso (FLasso), which is beneficial and helpful in the cases where the number of explanatory variables or features is greater than the sample size. FLasso is penalizing both the coefficients and their successive differences by the L-1-norm, and it allows sparsity for both of them, while Lasso only allows the coefficients by considering a nonsmooth optimization problem. In this study, we take into account Iterated Ridge approximation which enables us to use a smooth optimization for FLasso problem. Generated smooth optimization problem is solved by using one of continuous optimization techniques called Conic Quadratic Programming (CQP), which is enabling the utilization of interior point methods. The newly developed method is called Conic FLasso for classification by MSOM (C-FLasso-CLMSOM) and is applied to real world data set to show its performance.
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
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: 9
Citation - Scopus: 8
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
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: 17
Citation - Scopus: 17
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
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.