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Evaluating Clustering Meta-features for Classifier Recommendation

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Intelligent Systems (BRACIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073))

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Abstract

Data availability in a wide variety of domains has boosted the use of Machine Learning techniques for knowledge discovery and classification. The performance of a technique in a given classification task is significantly impacted by specific characteristics of the dataset, which makes the problem of choosing the most adequate approach a challenging one. Meta-Learning approaches, which learn from meta-features calculated from the dataset, have been successfully used to suggest the most suitable classification algorithms for specific datasets. This work proposes the adaptation of clustering measures based on internal indices for supervised problems as additional meta-features in the process of learning a recommendation system for classification tasks. The gains in performance due to Meta-Learning and the additional meta-features are investigated with experiments based on 400 datasets, representing diverse application contexts and domains. Results suggest that (i) meta-learning is a viable solution for recommending a classifier, (ii) the use of clustering features can contribute to the performance of the recommendation system, and (iii) the computational cost of Meta-Learning is substantially smaller than that of running all candidate classifiers in order to select the best.

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Notes

  1. 1.

    https://github.com/rivolli/mfe.

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Acknowledgment

Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Brasília, Brasília, Brazil

    Luís P. F. Garcia & Guilherme N. Ramos

  2. College of Engineering and Physical Sciences, Aston University, Birmingham, UK

    Felipe Campelo

  3. Computing Department, Technological University of Paraná, Cornélio Procópio, Brazil

    Adriano Rivolli

  4. Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil

    André C. P. de L. F. de Carvalho

Authors
  1. Luís P. F. Garcia
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  2. Felipe Campelo
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  3. Guilherme N. Ramos
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  4. Adriano Rivolli
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  5. André C. P. de L. F. de Carvalho
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Corresponding author

Correspondence to Luís P. F. Garcia .

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Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

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Garcia, L.P.F., Campelo, F., Ramos, G.N., Rivolli, A., de Carvalho, A.C.P.d.L.F. (2021). Evaluating Clustering Meta-features for Classifier Recommendation. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-91702-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91701-2

  • Online ISBN: 978-3-030-91702-9

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