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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 Determinig the ligand-specific regions of peptide-binding G-Protein Coupled Receptors(2010) Çobanoǧlu, Murat Can; Sezerman, Osman Uğur; Karabulut, Nermin Pinar; Çobanoǧlu, Murat Can, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Sezerman, Osman Uğur, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Karabulut, Nermin Pinar, Department of Computer Science, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe G-Protein Coupled Receptor (GPCR) proteins are of paramount importance for pharmaceutical research as this single family of proteins is targeted by more than 50% of all modern drugs. Their ligands include but are not limited to small molecules, peptides and photons. Consequently it is vital to know the sites of interaction between the ligand and the receptor. These sites can be used for a multitude of purposes such as classification of sequences or drug design. We use the feature extraction method proposed in [1] to analyze the ligand-specific regions among various Peptide-binding GPCRs. We present the features that will help drug design researchers enhance specificity of peptide-binding drugs. © 2009 IEEE. © 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 Use of line spectral frequencies for emotion recognition from speech(2010) Bozkurt, Elif; Erzin, Engin; Eroğlu Erdem, Çiğdem; Erdem, Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, TurkeyWe propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker [4]. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Prediction of protein sub-nuclear location by clustering mRMR ensemble feature selection(2010) Sakar, C. Okan; Kursun, Olcay; Şeker, Hüseyin; Gürgen, Fïkret S.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Şeker, Hüseyin, Department of Informatics, De Montfort University, Leicester, United Kingdom; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, TurkeyIn many applications of pattern recognition in the bioinformatics and biomedical fields, input variables are organized into natural partitions that are called views in the literature. Mutual information can be used in selecting a minimal yet capable subset of views. Ignoring the presence of views, dismantling them, and treating their variables intermixed along with those of others at best results in a complex uninterpretable predictive system for researchers in these fields. Moreover, it would require measuring or computing majority of the views. We use the clustering indices of the views and rank the views according to the unique information they have with the target using minimum redundancy-maximum relevance (mRMR) approach. We also propose an ensemble approach to reduce the random variations in clusterings. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only INTERSPEECH 2009 emotion recognition challenge evaluation, INTERSPEECH 2009 duygu tanima yarişmasi deǧerlendirmesi(2010) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, TurkeyIn this paper we evaluate INTERSPEECH 2009 Emotion Recognition Challenge results. The challenge presents the problem of accurate classification of natural and emotionally rich FAU Aibo recordings into five and two emotion classes. We evaluate prosody related, spectral and HMM-based features with Gaussian mixture model (GMM) classifiers to attack this problem. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of pitch, first derivative of pitch and intensity. We employ unsupervised training of HMM structures with prosody related temporal features to define HMM-based features. We also investigate data fusion of different features and decision fusion of different classifiers to improve emotion recognition results. Our two-stage decision fusion method achieves 41.59 % and 67.90 % recall rate for the five and two-class problems, respectively and takes second and fourth place among the overall challenge results. ©2010 IEEE. © 2011 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.