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Öğe A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases(SPRINGER, 2012) Comak, Emre; Arslan, AhmetClassification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and sigma). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.Öğe Color Image Watermarking Scheme Based on Efficient Preprocessing and Support Vector Machines(SPRINGER-VERLAG BERLIN, 2008) Findik, Oguz; Bayrak, Mehmet; Babaoglu, Ismail; Comak, EmreThis paper suggests a new block based watermarking technique utilizing preprocessing and support vector machine (PPSVMW) to protect color image's intellectual property rights. Binary test set is employed here to train support vector machine (SVM). Before adding binary data into the original image, blocks have been separated into two parts to train SVM for better accuracy. Watermark's I valued bits were randomly added into the first block part and 0 into the second block part. Watermark is embedded by modifying the blue channel pixel value in the middle of each block so that watermarked image could be composed. SVM was trained with set-bits and three other features which are averages of the differences of pixels in three distinct shapes extracted from each block, and hence without the need of original image, it could be extracted. The results of PPSVMW technique proposed in this study were compared with those of the Tsai's technique. Our technique was proved to be more efficient.Öğe A decision support system based on support vector machines for diagnosis of the heart valve diseases(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Comak, Emre; Arslan, Ahmet; Turkoglu, IbrahimIn this paper, a decision support system that classifies the Doppler signals of the heart valve to two classes (normal and abnormal) is presented to support the cardiologist. The paper uses our previous paper where ANN is used as a classifier, as feature extractor from measured Doppler signal. To make this, it uses wavelet transforms and short time Fourier transform methods. Before it classifies these features, it applies Wavelet entropy to them. In this paper, our aim is to develop our previous work by using least-squares support vector machine (LS-SVM) classifier instead of ANN. We use LS-SVM and backpropagation artificial neural network (BP-ANN) to classify the extracted features. In addition, we use receiver operator characteristic (ROC) curves to compare sensitivities and specificities of these classifiers and compute the area under the curves. Finally, we evaluate two classifiers in all aspects. (c) 2005 Elsevier Ltd. All rights reserved.Öğe A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Comak, Emre; Polat, Kemal; Guenes, Salih; Arslan, AhmetThe use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. This study aims at diagnosing Liver Disorder with a new hybrid machine learning method. By hybridizing LSSVM with Fuzzy Weighting Pre-processing, a method was obtained to solve this diagnosis problem via classifying Liver Disorder. Fuzzy Weighting Pre-processing stage was developed firstly in our study. This Liver Disorder dataset is a very commonly used dataset in literature relating the use of classification systems for Liver Disorder Diagnosis and it was used in this study to compare the classification performance of our proposed method with regard other studies. We obtained a classification accuracy of 94.29%, which is the highest one reached so far. This result is for Liver Disorder but it states that this method can be used confidently for other medical diseases diagnosis problems, too. (C) 2005 Elsevier Ltd. All rights reserved.Öğe A new training method for support vector machines: Clustering k-NN support vector machines(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Comak, Emre; Arslan, AhmetFor training of support vector machines (SVMs) efficiently, a new training algorithm, clustering k-NN (k-nearest neighbor) support vector machines (CKSVMs) based on a Gaussian function regulated locally is proposed. In order to reflect degree of training data point as a support vector the Gaussian function is used with k-nearest neighbor (k-NN) method and Euclidean Distance measure. To add local control property to the training algorithm, a simple clustering scheme is implemented before Gaussian functions are constructed for each cluster. In addition, probabilistic SVM outputs are used for extension from binary classification to multi-class classification in pairwise approach. This training algorithm is applied to three commonly used classification problems. Experimental results show that the CKSVM has more classification accuracy than standard multi-class LS-SVM, FLS-SVM and LS-SVM with k-NN method which is proposed in our previous study. In addition to this, the training algorithm highly improved efficiency of the SVM classifier via simple algorithm. (c) 2007 Elsevier Ltd. All rights reserved.
