eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Mona Riemenschneider, Alexander Herbst, Ari Rasch, Sergei Gorlatch, Dominik Heider
Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, 94315 Straubing, Germany
MC Bioinformatics BMC series – open, inclusive and trusted 18:371, 2017


   title={eccCL: parallelized GPU implementation of Ensemble Classifier Chains},

   author={Riemenschneider, Mona and Herbst, Alexander and Rasch, Ari and Gorlatch, Sergei and Heider, Dominik},

   journal={BMC bioinformatics},





   publisher={BioMed Central}


BACKGROUND: Multi-label classification has recently gained great attention in diverse fields of research, e.g., in biomedical application such as protein function prediction or drug resistance testing in HIV. In this context, the concept of Classifier Chains has been shown to improve prediction accuracy, especially when applied as Ensemble Classifier Chains. However, these techniques lack computational efficiency when applied on large amounts of data, e.g., derived from next-generation sequencing experiments. By adapting algorithms for the use of graphics processing units, computational efficiency can be greatly improved due to parallelization of computations. RESULTS: Here, we provide a parallelized and optimized graphics processing unit implementation (eccCL) of Classifier Chains and Ensemble Classifier Chains. Additionally to the OpenCL implementation, we provide an R-Package with an easy to use R-interface for parallelized graphics processing unit usage. CONCLUSION: eccCL is a handy implementation of Classifier Chains on GPUs, which is able to process up to over 25,000 instances per second, and thus can be used efficiently in high-throughput experiments. The software is available.
Rating: 2.0/5. From 1 vote.
Please wait...

* * *

* * *

HGPU group © 2010-2021 hgpu.org

All rights belong to the respective authors

Contact us: