Abstract
This chapter proposes a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.
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Akama, S., Kudo, Y., Murai, T. (2020). A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables. In: Topics in Rough Set Theory. Intelligent Systems Reference Library, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-030-29566-0_6
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DOI: https://doi.org/10.1007/978-3-030-29566-0_6
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