Abstract
High dimensional data sets pose important challenges such as the curse of dimensionality and increased computational costs. Dimensionality reduction is therefore a crucial step for most data mining applications. Feature selection techniques allow us to achieve said reduction. However, it is nowadays common to deal with huge data sets, and most existing feature selection algorithms are designed to function in a centralized fashion, which makes them non scalable. Moreover, some of them require the selection process to be validated according to some target, which constrains their applicability to the supervised learning setting. In this paper we propose as novelty a parallel, scalable, exact implementation of an existing centralized, unsupervised feature selection algorithm on Spark, an efficient big data framework for large-scale distributed computation that outperforms MapReduce when applied to multi-pass algorithms. We validate the efficiency of the implementation using 1GB of real Internet traffic captured at a medium-sized ISP.
The research leading to these results has been developed within the ONTIC project, which has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 619633.
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Ordozgoiti, B., Gómez Canaval, S., Mozo, A. (2015). Massively Parallel Unsupervised Feature Selection on Spark. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_21
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DOI: https://doi.org/10.1007/978-3-319-23201-0_21
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