Multiple classifier systems : 9th international workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010 ; proceedings / Neamat El Gayar, Josef Kittler, Fabio Roli (eds.).

By: (9th : MCS (Workshop) (9th : 2010 : Cairo, Egypt)
Contributor(s): El Gayar, Neamat | Kittler, Josef, 1946- | Roli, Fabio, 1962-
Material type: TextTextSeries: SerienbezeichnungLecture notes in computer science: 5997.; LNCS sublibrary: Publisher: Berlin : Springer, 2010Description: 1 online resource (x, 328 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783642121272; 3642121276; 9783642121265; 3642121268Subject(s): Machine learning -- Congresses | Neural networks (Computer science) -- Congresses | Pattern perception -- Congresses | Informatique | Machine learning | Neural networks (Computer science) | Pattern perception | Classification -- Congresses | Neural Networks (Computer) -- Congresses | Pattern Recognition, Automated -- Congresses | Classification | Neural Networks, Computer | Pattern Recognition, AutomatedGenre/Form: Electronic books. | Conference papers and proceedings. | Congress. | Electronic books. Additional physical formats: Print version:: Multiple classifier systems.DDC classification: 006.3/1 LOC classification: Q325.5 | .M37 2010Online resources: Click here to access online
Contents:
Classifier ensembles (I). Weighted bagging for graph based one-class classifiers / Santi Seguí, Laura Igual, and Jordi Vitrià -- Improving multilabel classification performance by using ensemble of multi-label classifiers / Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, and Fei Yan -- New feature splitting criteria for co-training using genetic algorithm optimization / Ahmed Salaheldin and Neamat El Gayar -- Incremental learning of new classes in unbalanced datasets : Learn++. UDNC / Gregory Ditzler, Michael D. Muhlbaier, and Robi Polikar -- Tomographic considerations in ensemble bias/variance decomposition / David Windridge -- Choosing parameters for random subspace ensembles for fMRI classification / Ludmila I. Kuncheva and Catrin O. Plumpton -- Classifier ensembles (II). An experimental study on ensembles of functional trees / Juan J. Rodríguez, César García-Osorio, Jesús Maudes, and José Francisco Díez-Pastor -- Multiple classifier systems under attack / Battista Biggio, Giorgio Fumera, and Fabio Roli -- SOCIAL : self-organizing classIfier ensemble for adversarial learning / Francesco Gargiulo and Carlo Sansone -- Unsupervised change-detection in retinal Images by a multiple-classifier approach / Giulia Troglio, Marina Alberti, Jón Atli Benediksson, Gabriele Moser, Sebastiano Bruno Serpico, and Einar Stefánsson -- A double pruning algorithm for classification ensembles / Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, and Alberto Suárez -- Estimation of the number of clusters using multiple clustering validity indices / Krzysztof Kryszczuk and Paul Hurley -- Classifier diversity. "Good" and "bad" diversity in majority vote ensembles / Gavin Brown and Ludmila I. Kuncheva -- Multi-information ensemble diversity / Zhi-Hua Zhou and Nan Li -- Classifier selection. Dynamic selection of ensembles of classifiers using contextual information / Paulo R. Cavalin, Robert Sabourin, and Ching Y. Suen -- Selecting structural base classifiers for graph-based multiple classifier systems / Wan-Jui Lee, Robert P.W. Duin, and Horst Bunke -- Combining multiple kernels. A support kernel machine for supervised selective combining of diverse pattern-recognition modalities / Alexander Tatarchuk, Eugene Urlov, Vadim Mottl, and David Windridge -- Combining multiple kernels by augmenting the kernel matrix / Fei Yan, Krystian Mikolajczyk, Josef Kittler, and Muhammad Atif Tahir -- Boosting and bootstrapping. Class-separability weighting and bootstrapping in error correcting outset code ensembles / R.S. Smith and T. Windeatt -- Boosted geometry-based ensembles / Oriol Pujol -- Online non-stationary boosting / Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, and Gavin Brown -- Handwriting recognition. Combining neural networks to improve performance of handwritten keyword spotting / Volkmar Frinken, Andreas Fischer, and Horst Bunke -- Combining committee-based semi-supervised and active learning and its application to handwritten digits recognition / Mohamed Farouk Abdel Hady and Friedhelm Schwenker -- Using diversity in classifier set selection for arabic handwritten recognition / Nabiha Azizi, Nadir Farah, Mokhtar Sellami, and Abdel Ennaji -- Applications. Forecast combination strategies for handling structural breaks for time series forecasting / Waleed M. Azmy, Amir F. Atiya, and Hisham El-Shishiny -- A multiple classifier system for classification of LIDAR remote sensing data using multi-class SVM / Farhad Samadzadegan, Behnaz Bigdeli, and Pouria Ramzi -- A multi-classifier system for off-line signature verification based on dissimilarity representation / Luana Batista, Eric Granger, and Robert Sabourin -- A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression / Noha A. Yousri -- Combining 2D and 3D features to classify protein mutants in HeLa cells / Carlo Sansone, Vincenzo Paduano, and Michele Ceccarelli -- An experimental comparison of hierarchical bayes and true path rule ensembles for protein function prediction / Matteo Re and Giorgio Valentini -- Recognizing combinations of facial action units with different intensity using a mixture of hidden Markov models and neural network / Mahmoud Khademi, Mohammad Taghi Manzuri-Shalmani, Mohammad Hadi Kiapour, and Ali Akbar Kiaei -- Invited papers. Some thoughts at the interface of ensemble methods and feature selection (abstract) / Gavin Brown -- Multiple classifier systems for the recogonition of human emotions / Friedhelm Schwenker, Stefan Scherer, Miriam Schmidt, Martin Schels, and Michael Glodek -- Erratum.
Summary: This book constitutes the proceedings of the 9th International Workshop on Multiple Classifier Systems, MCS 2010, held in Cairo, Egypt, in April 2010. The 31 papers presented were carefully reviewed and selected from 50 submissions. The contributions are organized into sessions dealing with classifier combination and classifier selection, diversity, bagging and boosting, combination of multiple kernels, and applications.
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Includes bibliographical references and author index.

Classifier ensembles (I). Weighted bagging for graph based one-class classifiers / Santi Seguí, Laura Igual, and Jordi Vitrià -- Improving multilabel classification performance by using ensemble of multi-label classifiers / Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, and Fei Yan -- New feature splitting criteria for co-training using genetic algorithm optimization / Ahmed Salaheldin and Neamat El Gayar -- Incremental learning of new classes in unbalanced datasets : Learn++. UDNC / Gregory Ditzler, Michael D. Muhlbaier, and Robi Polikar -- Tomographic considerations in ensemble bias/variance decomposition / David Windridge -- Choosing parameters for random subspace ensembles for fMRI classification / Ludmila I. Kuncheva and Catrin O. Plumpton -- Classifier ensembles (II). An experimental study on ensembles of functional trees / Juan J. Rodríguez, César García-Osorio, Jesús Maudes, and José Francisco Díez-Pastor -- Multiple classifier systems under attack / Battista Biggio, Giorgio Fumera, and Fabio Roli -- SOCIAL : self-organizing classIfier ensemble for adversarial learning / Francesco Gargiulo and Carlo Sansone -- Unsupervised change-detection in retinal Images by a multiple-classifier approach / Giulia Troglio, Marina Alberti, Jón Atli Benediksson, Gabriele Moser, Sebastiano Bruno Serpico, and Einar Stefánsson -- A double pruning algorithm for classification ensembles / Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, and Alberto Suárez -- Estimation of the number of clusters using multiple clustering validity indices / Krzysztof Kryszczuk and Paul Hurley -- Classifier diversity. "Good" and "bad" diversity in majority vote ensembles / Gavin Brown and Ludmila I. Kuncheva -- Multi-information ensemble diversity / Zhi-Hua Zhou and Nan Li -- Classifier selection. Dynamic selection of ensembles of classifiers using contextual information / Paulo R. Cavalin, Robert Sabourin, and Ching Y. Suen -- Selecting structural base classifiers for graph-based multiple classifier systems / Wan-Jui Lee, Robert P.W. Duin, and Horst Bunke -- Combining multiple kernels. A support kernel machine for supervised selective combining of diverse pattern-recognition modalities / Alexander Tatarchuk, Eugene Urlov, Vadim Mottl, and David Windridge -- Combining multiple kernels by augmenting the kernel matrix / Fei Yan, Krystian Mikolajczyk, Josef Kittler, and Muhammad Atif Tahir -- Boosting and bootstrapping. Class-separability weighting and bootstrapping in error correcting outset code ensembles / R.S. Smith and T. Windeatt -- Boosted geometry-based ensembles / Oriol Pujol -- Online non-stationary boosting / Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, and Gavin Brown -- Handwriting recognition. Combining neural networks to improve performance of handwritten keyword spotting / Volkmar Frinken, Andreas Fischer, and Horst Bunke -- Combining committee-based semi-supervised and active learning and its application to handwritten digits recognition / Mohamed Farouk Abdel Hady and Friedhelm Schwenker -- Using diversity in classifier set selection for arabic handwritten recognition / Nabiha Azizi, Nadir Farah, Mokhtar Sellami, and Abdel Ennaji -- Applications. Forecast combination strategies for handling structural breaks for time series forecasting / Waleed M. Azmy, Amir F. Atiya, and Hisham El-Shishiny -- A multiple classifier system for classification of LIDAR remote sensing data using multi-class SVM / Farhad Samadzadegan, Behnaz Bigdeli, and Pouria Ramzi -- A multi-classifier system for off-line signature verification based on dissimilarity representation / Luana Batista, Eric Granger, and Robert Sabourin -- A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression / Noha A. Yousri -- Combining 2D and 3D features to classify protein mutants in HeLa cells / Carlo Sansone, Vincenzo Paduano, and Michele Ceccarelli -- An experimental comparison of hierarchical bayes and true path rule ensembles for protein function prediction / Matteo Re and Giorgio Valentini -- Recognizing combinations of facial action units with different intensity using a mixture of hidden Markov models and neural network / Mahmoud Khademi, Mohammad Taghi Manzuri-Shalmani, Mohammad Hadi Kiapour, and Ali Akbar Kiaei -- Invited papers. Some thoughts at the interface of ensemble methods and feature selection (abstract) / Gavin Brown -- Multiple classifier systems for the recogonition of human emotions / Friedhelm Schwenker, Stefan Scherer, Miriam Schmidt, Martin Schels, and Michael Glodek -- Erratum.

Print version record.

This book constitutes the proceedings of the 9th International Workshop on Multiple Classifier Systems, MCS 2010, held in Cairo, Egypt, in April 2010. The 31 papers presented were carefully reviewed and selected from 50 submissions. The contributions are organized into sessions dealing with classifier combination and classifier selection, diversity, bagging and boosting, combination of multiple kernels, and applications.

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