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Type-2 Mamdani Fuzzy System Optimization for a Classification Ensemble with Black Widow Optimizer

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New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1149))

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Abstract

Ensemble models perform classification on data using multiple models combined and unified to a single output. An important part of the ensemble is the combination or aggregation process. Even so the design of the aggregator for each ensemble could be very different depending on the problem. For this case, a proposed aggregation algorithm combines two classification submodels using a Mamdani fuzzy system. Depending on the data to learn, the fuzzy system could be designed differently. To solve this, we applied a Bio-inspired optimization algorithm, the Black Widow Optimizer, to adjust the fuzzy system into the data the ensemble learns. Adjustment to the problem is done by obtaining the best fuzzy system parameters including membership function points, type of function and optimizing the fuzzy rules. The optimization is compared with other optimization algorithms and experiments for the ensemble are done on two classification datasets of medical images.

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Acknowledgements

we would like to express our gratitude to CONACYT, Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, TecNM, Tijuana, BC, Mexico

    Sergio Varela-Santos & Patricia Melin

Authors
  1. Sergio Varela-Santos
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  2. Patricia Melin
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Correspondence to Patricia Melin .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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Varela-Santos, S., Melin, P. (2024). Type-2 Mamdani Fuzzy System Optimization for a Classification Ensemble with Black Widow Optimizer. In: Castillo, O., Melin, P. (eds) New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-031-55684-5_3

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