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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 Open Access Using covariates for improving the minimum Redundancy Maximum Relevance feature selection method(2010) Kurşun, Olcay; Şakar, C. Okan; Favorov, Oleg Vyachesslavovich; Aydın, Nizamettin; Gürgen, Fikret S.; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Favorov, Oleg Vyachesslavovich, Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, United States; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, Turkey; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, TurkeyMaximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets. © TÜBİTAK. © 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 Automatic grading of Bi-colored apples by multispectral machine vision(2011) Ünay, Devrim; Gosselin, Bernard; Kleynen, Olivier; Leemans, Vincent; Destain, Marie France; Debeir, Olivier; Ünay, Devrim, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gosselin, Bernard, TCTS Lab., Université de Mons - Faculté Polytechnique de Mons, Mons, Belgium; Kleynen, Olivier, Dept. of Mech., Université de Liège, Liege, Belgium; Leemans, Vincent, Dept. of Mech., Université de Liège, Liege, Belgium; Destain, Marie France, Dept. of Mech., Université de Liège, Liege, Belgium; Debeir, Olivier, Information and Decision Systems, Université Libre de Bruxelles, Brussels, BelgiumIn this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two- and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate. © 2010 Elsevier B.V. © 2011 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 A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method(2012) Sakar, C. Okan; Kursun, Olcay; Gürgen, Fïkret S.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, TurkeyIn this paper, we propose a feature selection method based on a recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets. © 2011 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V., All rights reserved.Publication Open Access Selection of representative SNP sets for genome-wide association studies: A metaheuristic approach(2012) Üstünkar, Gürkan; Akyüz, Süreyya; Weber, Gerhard Wilhelm; Friedrich, Christoph M.; Son, Yeşim Aydın; Üstünkar, Gürkan, Department of Information Systems, Middle East Technical University (METU), Ankara, Turkey; Akyüz, Süreyya, Department of Mathematics and Computer Science, Bahçeşehir Üniversitesi, Istanbul, Turkey; Weber, Gerhard Wilhelm, Institute of Applied Mathematics, Middle East Technical University (METU), Ankara, Turkey; Friedrich, Christoph M., Department of Computer Science, Fachhochschule Dortmund, Dortmund, Germany; Son, Yesim Aydin, Department of Health Informatics, Middle East Technical University (METU), Ankara, TurkeyAfter the completion of Human Genome Project in 2003, it is now possible to associate genetic variations in the human genome with common and complex diseases. The current challenge now is to utilize the genomic data efficiently and to develop tools to improve our understanding of etiology of complex diseases. Many of the algorithms needed to deal with this task were originally developed in management science and operations research (OR). One application is to select a subset of the Single Nucleotide Polymorphism (SNP) biomarkers from the whole SNP set that is informative and small enough for subsequent association studies. In this paper, we present an OR application for representative SNP selection that implements our novel Simulated Annealing (SA) based feature-selection algorithm. We hope that our work will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases and ultimately support timely identification of genomic disease biomarkers and the development of personalized-medicine approaches and targeted drug discoveries. © 2011 Springer-Verlag. © 2012 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.