Abstract
The classification problem involves classifying an observation into one of two groups based on its attributes.Determination of a hyperplane which misclassifies the fewest number of observations from the training sample is a hard combinatorial optimization problem and the sequential algorithms for it are still not practical for large size instances. We propose a parallel branch-and-bound algorithm for the problem on distributed-memory MIMD architectures.It achieves a good load balance and a small communication overhead by using a simple load balancing scheme.Our approach is validated by experimental results.
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© 1999 Springer-Verlag Berlin Heidelberg
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Balev, S., Andonov, R., Freville, A. (1999). A Parallel Branch-and-Bound Algorithm for the Classification Problem. In: Banerjee, P., Prasanna, V.K., Sinha, B.P. (eds) High Performance Computing – HiPC’99. HiPC 1999. Lecture Notes in Computer Science, vol 1745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46642-0_40
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DOI: https://doi.org/10.1007/978-3-540-46642-0_40
Publisher Name: Springer, Berlin, Heidelberg
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