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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 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 Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection(2011) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Department of Electrical Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed. © 2011 IEEE. © 2012 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 Metadata only A method for combining mutual information and canonical correlation analysis: Predictive Mutual Information and its use in feature selection(2012) 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, TurkeyFeature selection is a critical step in many artificial intelligence and pattern recognition problems. Shannon's Mutual Information (MI) is a classical and widely used measure of dependence measure that serves as a good feature selection algorithm. However, as it is a measure of mutual information in average, under-sampled classes (rare events) can be overlooked by this measure, which can cause critical false negatives (missing a relevant feature very predictive of some rare but important classes). Shannon's mutual information requires a well sampled database, which is not typical of many fields of modern science (such as biomedical), in which there are limited number of samples to learn from, or at least, not all the classes of the target function (such as certain phenotypes in biomedical) are well-sampled. 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 a hybrid measure of relevance, Predictive Mutual Information (PMI) based on MI, which also accounts for predictability of signals from each other in its calculation as in KCCA. We show that PMI has more improved feature detection capability than MI, especially in catching suspicious coincidences that are rare but potentially important not only for experimental studies but also for building computational models. We demonstrate the usefulness of PMI, and superiority over MI, on both toy and real datasets. © 2011 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction for facial expression recognition by canonical correlation analysis, Kanoni̇k korelasyon anali̇zi̇ i̇le yüz i̇fadesi̇nden duygu tanima i̇çi̇n özni̇teli̇ k çikarimi(2012) Sakar, C. Okan; Kursun, Olcay; Karaali, Ali; Erdem, Cigdem Eroglu; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Karaali, Ali, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyAlthough several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction based on discriminative alternating regression(Springer Verlag [email protected], 2014) 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, TurkeyCanonical Correlation Analysis (CCA) aims at measuring linear relationships between two sets of variables (views). Recently, CCA has been used for feature extraction in classification problems with multiview data by means of view fusion. However, the extracted correlated features with CCA may not be discriminative since CCA does not utilize the class labels in its traditional formulation. Besides, the CCA features are computed based on within-set and between-set sample covariance matrices of the views which can be very sensitive to representation-specific details and noisy samples of the two views. In this paper, we propose a method, D-AR (Discriminative Alternating Regression), in which the two above-mentioned problems encountered in the application of CCA for feature extraction are addressed: (1) the class labels are incorporated into the proposed feature fusion framework to explore correlated and also discriminative features, and (2) the use of sensitive sample covariates matrices is avoided while fusing the two views. D-AR is a supervised feature fusion approach based on Multi-layer Perceptron (MLP) implementation of alternating regression. From the neurobiological perspective, the architecture of D-AR is similar to the model of a single neuron in the cerebral cortex which has a function of discovering and representing one of the hidden factors in its sensory environment. The MLP trained on each view aims to predict the class labels and also the hidden factors which are responsible for the correlation. We show that the features found by D-AR on training sets accomplishes significantly higher classification accuracies on test set of an experimental dataset. © Springer International Publishing Switzerland 2014. © 2017 Elsevier B.V., All rights reserved.Publication Metadata only Discriminative feature extraction by a neural implementation of canonical correlation analysis(Institute of Electrical and Electronics Engineers Inc., 2017) 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, TurkeyThe canonical correlation analysis (CCA) aims at measuring linear relationships between two sets of variables (views) that can be used for feature extraction in classification problems with multiview data. However, the correlated features extracted by the CCA may not be class discriminative, since CCA does not utilize the class labels in its traditional formulation. Although there is a method called discriminative CCA (DCCA) that aims to increase the discriminative ability of CCA inspired from the linear discriminant analysis (LDA), it has been shown that the extracted features with this method are identical to those by the LDA with respect to an orthogonal transformation. Therefore, DCCA is simply equivalent to applying single-view (regular) LDA to each one of the views separately. Besides, DCCA and the other similar DCCA approaches have generalization problems due to the sample covariance matrices used in their computation, which are sensitive to outliers and noisy samples. In this paper, we propose a method, called discriminative alternating regression (D-AR), to explore correlated and also discriminative features. D-AR utilizes two (alternating) multilayer perceptrons, each with a linear hidden layer, learning to predict both the class labels and the outputs of each other. We show that the features found by D-AR on training sets significantly accomplish higher classification accuracies on test sets of facial expression recognition, object recognition, and image retrieval experimental data sets. © 2018 Elsevier B.V., All rights reserved.Publication Metadata only Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems(SciTePress, 2018) Tiryaki, Ozde; Sakar, C. Okan; Bernardino, J.; Quix, C.; Tiryaki, Ozde, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, Netas Telecommunications, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyCanonical Correlation Analysis (CCA) is a data analysis technique used to extract correlated features between two sets of variables. An important limitation of CCA is that it is a linear technique that cannot capture nonlinear relations in complex situations. To address this limitation, Kernel CCA (KCCA) has been proposed which is capable of identifying the nonlinear relations with the use of kernel trick. However, it has been shown that KCCA tends to overfit to the training set without proper regularization. Besides, KCCA is an unsupervised technique which does not utilize class labels for feature extraction. In this paper, we propose the nonlinear version of the discriminative alternating regression (D-AR) method to address these problems. While in linear D-AR two neural networks each with a linear bottleneck hidden layer are combined using alternating regression approach, the modified version of the linear D-AR proposed in this study has a nonlinear activation function in the hidden layers of the alternating multilayer perceptrons (MLP). Experimental results on a classification and a multiple-output regression problem with sigmoid and hyperbolic tangent activation functions show that features found by nonlinear D-AR from training examples accomplish significantly higher accuracy on test set than that of KCCA. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform(Elsevier Ltd, 2019) Sakar, C. Okan; Serbes, Görkem; Gündüz, Ayşegül Ş.; Tunc, Hunkar C.; Nizam-Ozogur, Hatice; Erdogdu Sakar, Betul; Tutuncu, Melih; Aydin, Tarkan; Isenkul, M. Erdem; Apaydin, H.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Serbes, Görkem, Department of Biomedical Engineering, Yıldız Teknik Üniversitesi, Istanbul, Turkey; Gündüz, Ayşegül Ş., Department of Neurology, İstanbul University-Cerrahpaşa Cerrahpaşa Faculty of Medicine, Istanbul, Turkey; Tunc, Hunkar C., Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Nizam-Ozogur, Hatice, Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey; Erdogdu Sakar, Betul, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Tutuncu, Melih, Department of Neurology, İstanbul University-Cerrahpaşa Cerrahpaşa Faculty of Medicine, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Isenkul, M. Erdem, Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey; Apaydin, H., Department of Neurology, İstanbul University-Cerrahpaşa Cerrahpaşa Faculty of Medicine, Istanbul, TurkeyIn recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson's disease (PD) based on measuring the motor system disorders caused by the disease. As approximately 90% percent of PD patients exhibit some form of vocal disorders in the earlier stages of the disease, the recent PD telediagnosis studies focus on the detection of the vocal impairments from sustained vowel phonations or running speech of the subjects. In these studies, various speech signal processing algorithms have been used to extract clinically useful information for PD assessment, and the calculated features were fed to learning algorithms to construct reliable decision support systems. In this study, we apply, to the best of our knowledge for the first time, the tunable Q-factor wavelet transform (TQWT) to the voice signals of PD patients for feature extraction, which has higher frequency resolution than the classical discrete wavelet transform. We compare the effectiveness of TQWT with the state-of-the-art feature extraction methods used in diagnosis of PD from vocal disorders. For this purpose, we have collected the voice recordings of 252 subjects in the context of this study and extracted multiple feature subsets from the voice recordings. The feature subsets are fed to multiple classifiers and the predictions of the classifiers are combined with ensemble learning approaches. The results show that TQWT performs better or comparable to the state-of-the-art speech signal processing techniques used in PD classification. We also find that Mel-frequency cepstral and the tunable-Q wavelet coefficients, which give the highest accuracies, contain complementary information in PD classification problem resulting in an improved system when combined using a filter feature selection technique. © 2018 Elsevier B.V., All rights reserved.