Widening : using parallel resources to improve model quality

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2021
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Siebes, Arno
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http://nbn-resolving.de/urn:nbn:de:bsz:352-2-jy2is72qj7cn0
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https://doi.org/10.1007/s10618-021-00749-5
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CC BY 4.0

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Data Mining and Knowledge Discovery. Springer. 2021, 35(4), pp. 1258-1286. ISSN 1384-5810. eISSN 1573-756X. Available under: doi: 10.1007/s10618-021-00749-5
Zusammenfassung

This paper provides a unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality. We discuss different theoretical approaches to Widening with and without consideration of diversity. We then soften some of the underlying constraints so that Widening can be implemented in real world algorithms. We summarize earlier experimental results demonstrating the potential impact as well as promising implementation strategies before concluding with a survey of related work.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Widening, Machine learning, Data mining, Algorithms, Parallelization
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undefined / . - undefined, undefined
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ISO 690BERTHOLD, Michael R., Alexander FILLBRUNN, Arno SIEBES, 2021. Widening : using parallel resources to improve model quality. In: Data Mining and Knowledge Discovery. Springer. 2021, 35(4), pp. 1258-1286. ISSN 1384-5810. eISSN 1573-756X. Available under: doi: 10.1007/s10618-021-00749-5
BibTex
@article{Berthold2021-07Widen-53453,
  year={2021},
  doi={10.1007/s10618-021-00749-5},
  title={Widening : using parallel resources to improve model quality},
  number={4},
  volume={35},
  issn={1384-5810},
  journal={Data Mining and Knowledge Discovery},
  pages={1258--1286},
  author={Berthold, Michael R. and Fillbrunn, Alexander and Siebes, Arno}
}
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