Yazar "Gueven, Ayseguel" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Gueven, Ayseguel; Polat, Kemal; Kara, Sadik; Guenes, Salih
    In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEPs) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals. (C) 2007 Elsevier Ltd. All rights reserved.
  • Küçük Resim Yok
    Öğe
    Utilization of Discretization method on the diagnosis of optic nerve disease
    (ELSEVIER IRELAND LTD, 2008) Polat, Kemal; Kara, Sadik; Gueven, Ayseguel; Guenes, Salih
    The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system. (C) 2008 Elsevier Ireland Ltd. All rights reserved.