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International Conference on Parallel Problem Solving from Nature

PPSN 2018: Parallel Problem Solving from Nature – PPSN XV pp 209–222Cite as

Filtering Outliers in One Step with Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

Abstract

Outliers are one of the most difficult issues when dealing with real-world modeling tasks. Even a small percentage of outliers can impede a learning algorithm’s ability to fit a dataset. While robust regression algorithms exist, they fail when a dataset is corrupted by more than 50% of outliers (breakdown point). In the case of Genetic Programming, robust regression has not been properly studied. In this paper we present a method that works as a filter, removing outliers from the target variable (vertical outliers). The algorithm is simple, it uses a randomly generated population of GP trees to determine which target values should be labeled as outliers. The method is highly efficient. Results show that it can return a clean dataset when contamination reaches as high as 90%, and may be able to handle higher levels of contamination. In this study only synthetic univariate benchmarks are used to evaluate the approach, but it must be stressed that no other approaches can deal with such high levels of outlier contamination while requiring such small computational effort.

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Notes

  1. 1.

    In fact, while the reported experiments only consider up to this level of data contamination, it is straightforward to extend our approach to more severe scenarios.

  2. 2.

    While it would be relatively simple to parallelize the algorithm, since all samples are taken independently, the cost can still become quite high if the modelling is done with GP.

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Acknowledgments

This research was funded by CONACYT (Mexico) Fronteras de la Ciencia 2015-2 Project No. FC-2015-2:944, BioISI R&D unit, UID/MULTI/04046/2013 funded by FCT/MCTES/PIDDAC, Portugal, and first author supported by CONACYT graduate scholarship No. 573397.

Author information

Authors and Affiliations

  1. Tecnológico Nacional de México/I.T. Tijuana, Tijuana, BC, Mexico

    Uriel López & Leonardo Trujillo

  2. BioISI, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

    Uriel López & Leonardo Trujillo

  3. IMB, UMR CNRS 5251, 351 cours de la libération, Talence, France

    Pierrick Legrand

  4. Inria Bordeaux Sud-Ouest, Talence, France

    Pierrick Legrand

  5. University of Bordeaux, Bordeaux, France

    Pierrick Legrand

Authors
  1. Uriel López
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  2. Leonardo Trujillo
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  3. Pierrick Legrand
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Corresponding author

Correspondence to Leonardo Trujillo .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

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López, U., Trujillo, L., Legrand, P. (2018). Filtering Outliers in One Step with Genetic Programming. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-99253-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99252-5

  • Online ISBN: 978-3-319-99253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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