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Publication Metadata only Augmenting clinical observations with visual features from longitudinal MRI data for improved dementia diagnosis(2010) Ünay, Devrim; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, TurkeyImage-based diagnosis in the medical area often requires qualitative interpretation from the experts, despite the high-resolution of the acquired images. Computation of quantitative measures and comparison of multiple patients using automated medical image analysis tools will help improve the diagnosis and efficiency, especially in the areas such as neurology, where diagnosis from one patient's data has limitations and the prevalence of neurodegenerative diseases is expected to substantially increase in the near future due to the aging population. To this end, this paper presents a novel work on fusing clinical and patient-demographics related observations with visual features computed from brain longitudinal MRI (magnetic resonance imaging) data for improved dementia diagnosis. Experiments with real data showed that augmenting cognitive scores with visual features from a subset of subcortical structures results in more accurate diagnosis. Moreover, subset of structures typically selected are consistent with those (being) investigated in the literature. Copyright 2010 ACM. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Enhanced feature selection from wavelet packet coefficients in fault diagnosis of induction motors with artificial neural networks(2010) Eren, Levent; Cekic, Yalcin; Devaney, Michael Joseph; Eren, Levent, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United StatesWavelet packet decomposition (WPD) of line current has been successfully applied in motor fault detection. Enhanced feature selection from wavelet packet coefficients (WPCs) is presented in this paper. This method involves the decomposition of motor current into equally spaced frequency bands by using an all-pass implementation of elliptic IIR half-band filters in the filter bank structure to obtain WPCs in a computationally efficient way. Then, the bias in WPCs for each frequency band is removed to suppress both power system harmonics and leakage from adjacent frequency bands. Finally, the enhanced features are used as inputs to an ANN to provide motor fault detection with higher fault detection rate. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only A hybrid method for feature selection based on mutual information and canonical correlation analysis(2010) Sakar, C. Okan; Kursun, Olcay; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, TurkeyMutual Information (MI) is a classical and widely used dependence measure that generally can serve as a good feature selection algorithm. However, under-sampled classes or rare but certain relations are overlooked by this measure, which can result in missing relevant features that could be very predictive of variables of interest, such as certain phenotypes or disorders in biomedical research, rare but dangerous factors in ecology, intrusions in network systems, etc. On the other hand, Kernel Canonical Correlation Analysis (KCCA) is a nonlinear correlation measure effectively used to detect independence but its use for feature selection or ranking is limited due to the fact that its formulation is not intended to measure the amount of information (entropy) of the dependence. In this paper, we propose Predictive Mutual Information (PMI), a hybrid measure of relevance not only is based on MI but also accounts for predictability of signals from one another as in KCCA. We show that PMI has more improved feature detection capability than MI and KCCA, especially in catching suspicious coincidences that are rare but potentially important not only for subsequent experimental studies but also for building computational predictive models which is demonstrated on two toy datasets and a real intrusion detection system dataset. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Open Access Simultaneous feature selection and ant colony clustering(2011) Akarsu, Emre; Karahoca, Adem; Akarsu, Emre, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Adem, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyClustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequential Backward Search feature selection technique is applied. Feature selection and clustering algorithms are incorporated as a Wrapper. The results show that, the accuracy of the FS-ACO clustering approach is better than the filter approaches. © 2010 Published by Elsevier Ltd. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Improving the performance of active voxel selection in the analysis of fMRI data using genetic algorithms(2013) Ülker, Ceyhun Can; Aytekin, Tevfik; Ülker, Ceyhun Can, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyRecent research has shown that it is possible to classify cognitive states of human subjects based on fMRI (functional magnetic resonance imaging) data. One of the obstacles in classifying fMRI data is the problem of high dimensionality. A single fMRI snapshot consists of thousands of voxels and since a single experiment contains many fMRI snapshots, the dimensionality of an fMRI data instance easily surpasses the order of tens of thousands. So, feature selection methods become a must from both classification and running time performance points of view. To this end several feature selection methods are studied, either general or specific to fMRI data. So far, one of the best such methods, which is specific to fMRI data, is called the active method [9]. In this work we combine genetic algorithms with the active method in order to improve the performance of feature selection. Specifically, we first reduce the feature dimension using the active method and search for informative features in that reduced space using genetic algorithms. We achieve better or similar levels of classification performance using a much smaller number of voxels than the active method offers. Copyright 2013 ACM. © 2014 Elsevier B.V., All rights reserved.Publication Metadata only Prediction of level and abrupt changes of ozon concentration, Ozon seviyesi ve ani deǧişimlerinin kestirimi(IEEE Computer Society [email protected], 2014) Develi, Ahmet; Kursun, Olcay; Erdogdu Sakar, Betul; Develi, Ahmet, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Erdogdu Sakar, Betul, Bahçeşehir Üniversitesi, Istanbul, TurkeyWhile, in stratosphere, high level ozone concentration protects the Earth against ultraviolet radiation, in lower troposphere it has negative effects on human health and environment. The goal of this study is to determine the feature groups that are related to abrupt changes in the level of ozone. Linear discriminant analysis and support vector machines methods are used to explore which combination of features are predictive of abrupt changes in ozone level on the simulation dataset collected in Ankara, Turkey, by an automatic air quality monitoring station operated by the ministry of environment and urban planning. The dataset consists of one year of measurements of air pollutants and the meteorological factors. The obtained results showed that particulate matters, nitric oxides and temperature are most effective parameters in the classification of absurt rise and fall in the level of ozone. © 2014 IEEE. © 2014 Elsevier B.V., All rights reserved.Publication Metadata only Evaluation of hybrid classification approaches: Case studies on credit datasets(Springer Verlag [email protected], 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, Taskin; Perner, P.; Cetiner, Erkan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Güngör, Vehbi Çağrı, Abdullah Gül Üniversitesi, Kayseri, Turkey; Kocak, Taskin, Bahçeşehir Üniversitesi, Istanbul, TurkeyHybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.Publication Metadata only Variable Importance Analysis in Default Prediction using Machine Learning Techniques(SciTePress, 2018) Gültekin, Basak; Erdogdu Sakar, Betul; Bernardino, J.; Quix, C.; Gültekin, Basak, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdogdu Sakar, Betul, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this study, different data mining techniques were applied to a finance credit data set from a financial institution to provide an automated and objective profitability measurement. Two-step methodology was used Determining the variables to be included in the model and deciding on the model to classify the potential credit application as bad credit (default) or good credit (not default). The phrases bad credit and good credit are used as class labels since they are used like this in financial sector jargon in Turkey. For this twostep procedure, different variable selection algorithms like Random Forest, Boruta and machine learning algorithms like Logistic Regression, Random Forest, Artificial Neural Network were tried. At the end of the feature selection phase, CRA and III variables were determined as most important variables. Moreover, occupation and product number were also predictor variables. For the classification phase, Neural Network model was the best model with higher accuracy and low average square error also Random Forest model better resulted than Logistic Regression model. © 2020 Elsevier B.V., All rights reserved.