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  • 1
    Language: English
    In: Applied Soft Computing Journal, 2011, Vol.11(2), pp.2334-2347
    Description: The need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best ( ) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. In the first approach, is updated (moved) with respect to a global estimation of the gradient of the underlying (error) surface or function and hence can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called , which can replace the native particle for a better guidance, the decision of which is held by a fair competition between the two. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the (not to the entire swarm), both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA parameters are well selected to fit the problem at hand. A major finding of the paper is that even if the SPSA parameters are not tuned well, results of SA-driven (SAD) PSO are still better than the best of PSO and SPSA. Since the problem of poor update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), both approaches are also integrated into MD-PSO and tested over a set of unsupervised data clustering applications. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.
    Keywords: Particle Swarm Optimization ; Stochastic Approximation ; Multi-Dimensional Search ; Gradient Descent ; Computer Science
    ISSN: 1568-4946
    E-ISSN: 18729681
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  • 2
    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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  • 3
    Language: English
    In: Expert Systems With Applications, 2011, Vol.38(3), pp.2212-2223
    Description: ► Multi-dimensional particle swarm optimization. ► Fractional global-best formation. ► Optimization in dynamic environments. ► Global optimum tracking. Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle ( ) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.
    Keywords: Particle Swarm Optimization ; Multi-Dimensional Search ; Fractional Global Best Formation ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 4
    Language: English
    In: Signal Processing: Image Communication, March 2014, Vol.29(3), pp.410-423
    Description: In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.
    Keywords: Video Shot Boundary Detection ; Human Perception ; Local Image Features ; Video Content Analysis ; Engineering ; Applied Sciences ; Computer Science
    ISSN: 0923-5965
    E-ISSN: 1879-2677
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  • 5
    Language: English
    In: Journal of Biomedical Informatics, June, 2014, Vol.49, p.16(16)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbi.2014.02.005 Byline: Serkan Kiranyaz, Turker Ince, Morteza Zabihi, Dilek Ince Abstract: The illustration of the proposed EEG classification system (top). The illustration of the evolution process of a CNBC (bottom). Display Omitted Article History: Received 29 June 2013; Accepted 3 February 2014
    Keywords: Electroencephalography
    ISSN: 1532-0464
    Source: Cengage Learning, Inc.
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  • 6
    Language: English
    In: Neurocomputing, 08 February 2017, Vol.224, pp.142-154
    Description: There are well-known limitations and drawbacks on the performance and robustness of the feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs). In this study we shall address them by Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and in this paper we shall show that this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. Experimental results show that POPs can scale up very well with the problem size and can have the potential to achieve a superior generalization performance on real benchmark problems with a significant gain.
    Keywords: Artificial Neural Networks ; Multi-Layer Perceptrons ; Progressive Operational Perceptrons ; Diversity ; Scalability ; Computer Science
    ISSN: 0925-2312
    E-ISSN: 1872-8286
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  • 7
    Language: English
    In: ISPRS Journal of Photogrammetry and Remote Sensing, April 2014, Vol.90, pp.10-22
    Description: Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.
    Keywords: Synthetic Aperture Radar ; Classification ; Image Analysis ; Visual Features ; Color ; Texture ; Engineering ; Geography
    ISSN: 0924-2716
    E-ISSN: 1872-8235
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  • 8
    Language: English
    In: IEEE Transactions on Geoscience and Remote Sensing, April 2014, Vol.52(4), pp.2197-2216
    Description: Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification.
    Keywords: Classification ; Color Features ; Evaluation ; Feature Extraction ; Polarimetric Radar ; Synthetic Aperture Radar (SAR) ; Engineering ; Physics
    ISSN: 0196-2892
    E-ISSN: 1558-0644
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  • 9
    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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  • 10
    Description: Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques. Comment: 14 pages, 5 figures, journal paper
    Keywords: Computer Science - Social And Information Networks ; Physics - Physics And Society
    Source: Cornell University
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