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MapReduce distributed highly random fuzzy forest for noisy big data | IEEE Conference Publication | IEEE Xplore

MapReduce distributed highly random fuzzy forest for noisy big data


Abstract:

Nowadays the amounts of data available to us have the ever larger growth trend. On the other hand such data often contain noise. We call them noisy Big Data. There is an ...Show More

Abstract:

Nowadays the amounts of data available to us have the ever larger growth trend. On the other hand such data often contain noise. We call them noisy Big Data. There is an increasing need for learning methods that can handle such noisy Big Data for classification tasks. In this paper we propose a highly random fuzzy forest algorithm for learning an ensemble of fuzzy decision trees from a big data set contaminated with attribute noise. We also present the distributed version of the proposed learning algorithm implemented in the MapReduce framework. Experiment results have demonstrated that the proposed algorithm is faster and more accurate than the state-of-the-art approach particularly in the presence of attribute noise.
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
ISBN Information:
Conference Location: Guilin, China

References

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