Abstract
Combining a hardware approach with a multiple classifier method can deeply improve system performance, since the multiple classifier system can successfully enhance the classification accuracy with respect to a single classifier, and a hardware implementation would lead to systems able to classify samples with high throughput and with a short latency. To the best of our knowledge, no paper in the literature takes into account the multiple classifier scheme as additional design parameter, mainly because of lack of efficient hardware combiner architecture.
In order to fill this gap, in this paper we will first propose a novel approach for an efficient hardware implementation of the majority voting combining rule. Then, we will illustrate a design methodology to suitably embed in a digital device a multiple classifier system having Decision Trees as base classifiers and a majority voting rule as combiner. Bagging, Boosting and Random Forests will be taken into account. We will prove the effectiveness of the proposed approach on two real case studies related to Big Data issues.
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Acknowledgments
The research leading to these results has been partially supported by the RoDyMan project, which has received funding from the European Research Council (FP7 IDEAS) under Advanced Grant agreement number 320992. The authors are solely responsible for its content. It does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of the information contained therein.
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Barbareschi, M., Del Prete, S., Gargiulo, F., Mazzeo, A., Sansone, C. (2015). Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_17
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DOI: https://doi.org/10.1007/978-3-319-20248-8_17
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