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Parallel Classification with Two-Stage Bagging Classifiers

In: Computing and Informatics, vol. 32, no. 4
V.c. Horak - T. Berka - M. Vajteršic

Details:

Year, pages: 2013, 661 - 677
Keywords:
Classification methods, bagging classifiers, parallel algorithms
About article:
Bootstrapped aggregation of classifiers, also referred to as bagging, is a classic meta-classification algorithm. We extend it to a two-stage architecture consisting of an initial voting amongst one-versus-all classifiers or single-class recognizers, and a second stage of one-versus-one classifiers or two-class discriminators used for disambiguation. Since our method constructs an ensemble of elementary classifiers, it lends itself very well to parallelization. We describe a static workload balancing strategy for embarrassingly parallel classifier construction as well as a parallelization of the classification process with the message passing interface. In our experiments, which are evaluated in terms of classification performance and speed-up, we obtained an up to three-fold increase in precision and significantly increased recall values.
How to cite:
ISO 690:
Horak, V., Berka, T., Vajteršic, M. 2013. Parallel Classification with Two-Stage Bagging Classifiers. In Computing and Informatics, vol. 32, no.4, pp. 661-677. 1335-9150.

APA:
Horak, V., Berka, T., Vajteršic, M. (2013). Parallel Classification with Two-Stage Bagging Classifiers. Computing and Informatics, 32(4), 661-677. 1335-9150.