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  • 2012  (8)
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  • 2012  (8)
  • 1
    Language: English
    In: Neural Networks, Oct, 2012, Vol.34, p.80(16)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neunet.2012.07.003 Byline: Serkan Kiranyaz, Toni Makinen, Moncef Gabbouj Abstract: In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. Article History: Received 17 December 2011; Revised 9 July 2012; Accepted 9 July 2012
    ISSN: 0893-6080
    Source: Cengage Learning, Inc.
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  • 2
    Language: English
    In: Expert Systems With Applications, 2012, Vol.39(5), pp.4710-4717
    Description: In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.
    Keywords: Polarimetric Synthetic Aperture Radar ; Radial Basis Function Network ; Particle Swarm Optimization ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 3
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
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  • 4
    Language: English
    In: Neural Networks, October 2012, Vol.34, pp.80-95
    Description: In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a “Divide and Conquer” approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases.
    Keywords: Audio Content-Based Classification ; Evolutionary Neural Networks ; Particle Swarm Optimization ; Multilayer Perceptron ; Computer Science
    ISSN: 0893-6080
    E-ISSN: 1879-2782
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  • 5
    Language: English
    In: EURASIP Journal on Audio, Speech, and Music Processing, 2012, Vol.2012(1), pp.1-23
    Description: A vast amount of audio features have been proposed in the literature to characterize the content of audio signals. In order to overcome specific problems related to the existing features (such as lack of discriminative power), as well as to reduce the need for manual feature selection, in this article, we propose an evolutionary feature synthesis technique with a built-in feature selection scheme. The proposed synthesis process searches for optimal linear/nonlinear operators and feature weights from a pre-defined multi-dimensional search space to generate a highly discriminative set of new (artificial) features. The evolutionary search process is based on a stochastic optimization approach in which a multi-dimensional particle swarm optimization algorithm, along with fractional global best formation and heterogeneous particle behavior techniques, is applied. Unlike many existing feature generation approaches, the dimensionality of the synthesized feature vector is also searched and optimized within a set range in order to better meet the varying requirements set by many practical applications and classifiers. The new features generated by the proposed synthesis approach are compared with typical low-level audio features in several classification and retrieval tasks. The results demonstrate a clear improvement of up to 15–20% in average retrieval performance. Moreover, the proposed synthesis technique surpasses the synthesis performance of evolutionary artificial neural networks , exhibiting a considerable capability to accurately distinguish among different audio classes.
    Keywords: Content-based retrieval ; Evolutionary computation ; Particle swarm optimization ; Feature selection ; Feature generation
    E-ISSN: 1687-4722
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  • 6
    Language: English
    Description: A generic and patient-specific classification system designed for robust and accurate detection of electrocardiogram (ECG) heartbeat patterns is presented. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. Due to its time–frequency localization properties, the wavelet transform is an efficient tool for analyzing nonstationary ECG signals which can be used to decompose an ECG signal according to scale, thus allowing separation of the relevant ECG waveform morphology descriptors from the noise, interference, baseline drift, and amplitude variation of the original signal. For the pattern recognition unit, feedforward and fully connected artificial neural networks (ANNs), which are optimally designed for each patient by the multidimensional particle swarm optimization (MD PSO) technique, are employed. Despite many promising ANN-based techniques have been applied to ECG signal classification, these classifier systems have not performed well in practice and their results have generally been limited to relatively small datasets mainly because such systems have in general static (fixed) network structures for classifiers. On the other hand, the proposed algorithm based on patient-adaptive architecture by means of an evolutionary classifier design has demonstrated significant performance improvement over such conventional global classifier systems. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
    Keywords: Engineering ; Biomedical Engineering ; Signal, Image and Speech Processing ; Cardiology ; Computational Intelligence ; Medicine ; Engineering
    ISBN: 9780857298676
    ISBN: 0857298674
    Source: SpringerLink Books
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  • 7
    Description: Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012...
    Source: DataCite
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  • 8
    Description: Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012...
    Source: DataCite
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