Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Information Retrieval
Machine Learning
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Automatic Text Categorization and Its Application to Text Retrieval
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeding-up Text Categorization in a GRID Computing Environment
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
On Text-based Mining with Active Learning and Background Knowledge Using SVM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Nursing-Care Freestyle Text Classification Using Support Vector Machines
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sparse Bayesian classification of predicate arguments
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Support vector machines for quality monitoring in a plastic injection molding process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mercatus: A Toolkit for the Simulation of Market-Based Resource Allocation Protocols in Grids
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
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Constructing a single text classifier that excels in any given application is a rather inviable goal. As a result, ensemble systems are becoming an important resource, since they permit the use of simpler classifiers and the integration of different knowledge in the learning process. However, many text-classification ensemble approaches have an extremely high computational burden, which poses limitations in applications in real environments. Moreover, state-of-the-art kernel-based classifiers, such as support vector machines and relevance vector machines, demand large resources when applied to large databases. Therefore, we propose the use of a new systematic distributed ensemble framework to tackle these challenges, based on a generic deployment strategy in a cluster distributed environment. We employ a combination of both task and data decomposition of the text-classification system, based on partitioning, communication, agglomeration, and mapping to define and optimize a graph of dependent tasks. Additionally, the framework includes an ensemble system where we exploit diverse patterns of errors and gain from the synergies between the ensemble classifiers. The ensemble data partitioning strategy used is shown to improve the performance of baseline state-of-the-art kernel-based machines. The experimental results show that the performance of the proposed framework outperforms standard methods both in speed and classification.