Distributed Text Classification With an Ensemble Kernel-Based Learning Approach

• Published in: IEEE transactions on human-machine systems 2010-05, Vol.40 (3), p.287-297
• Main Authors: Silva, Catarina, Lotric, Uros, Ribeiro, Bernardete, Dobnikar, Andrej
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Availability; Classification; Classifiers; Computer science; control theory; systems; Data processing. List processing. Character string processing; Distributed learning; Educational technology; ensembles; Exact sciences and technology; Hardware; Informatics; Information systems. Data bases; kernel-based machines; Learning; Learning and adaptive systems; Machine learning; Memory organisation. Data processing; Partitioning; Production; Software; Speech and sound recognition and synthesis. Linguistics; State of the art; Strategy; Studies; Support vector machine classification; Support vector machines; Tasks; Text categorization; text classification; Texts
• ISSN: 1094-6977; 2168-2291; 1558-2442; 2168-2305
• DOI: 10.1109/TSMCC.2009.2038280

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.