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Abstract

A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining.

The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications.

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Notes

  1. 1.

    http://jmetal.github.io/jMetal/.

  2. 2.

    https://simidat.ujaen.es/papers/ManyObjectiveEFEP/.

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Acknowledgement

This study was funded by the FPI 2016 Scholarship reference BES-2016-077738 (FEDER Founds).

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Correspondence to Cristobal J. Carmona .

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Garcia-Vico, A.M., Carmona, C.J., Gonzalez, P., del Jesus, M.J. (2021). A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_10

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