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Ranking of Classification Algorithms in Terms of Mean–Standard Deviation Using A-TOPSIS

Author

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  • André G. C. Pacheco

    (UFES - Federal University of Espirito Santo)

  • Renato A. Krohling

    (UFES - Federal University of Espirito Santo
    UFES - Federal University of Espirito Santo)

Abstract

In classification problems when multiple algorithms are applied to different benchmarks a difficult issue arises, i.e., how can we rank the algorithms? In machine learning, it is common to run the algorithms several times and then a statistic is calculated in terms of means and standard deviations. In order to compare the performance of the algorithms, it is very common to employ statistical tests. However, these tests may also present limitations, since they consider only the means and not the standard deviations of the obtained results. In this paper, we present the so-called A-TOPSIS, based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to solve the problem of ranking and comparing classification algorithms in terms of means and standard deviations. We use two case studies to illustrate the A-TOPSIS for ranking classification algorithms and the results show the suitability of A-TOPSIS to rank the algorithms. The presented approach can be applied to compare the performance of stochastic algorithms in machine learning. Lastly, to encourage researchers to use the A-TOPSIS for ranking algorithms, we also presented in this work an easy-to-use A-TOPSIS web framework.

Suggested Citation

  • André G. C. Pacheco & Renato A. Krohling, 2018. "Ranking of Classification Algorithms in Terms of Mean–Standard Deviation Using A-TOPSIS," Annals of Data Science, Springer, vol. 5(1), pages 93-110, March.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:1:d:10.1007_s40745-018-0136-5
    DOI: 10.1007/s40745-018-0136-5
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    References listed on IDEAS

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    1. Peng, Yi & Kou, Gang & Wang, Guoxun & Shi, Yong, 2011. "FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms," Omega, Elsevier, vol. 39(6), pages 677-689, December.
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    1. Nikhil J. Rathod & Manoj K. Chopra & Prem Kumar Chaurasiya & Umesh S. Vidhate & Abhishek Dasore, 2023. "Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS," Annals of Data Science, Springer, vol. 10(5), pages 1405-1419, October.

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