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Performance-Interpretability Tradeoff of Mamdani Neuro-Fuzzy Classifiers for Medical Data

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Information Systems and Technologies (WorldCIST 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 468))

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Abstract

Neuro-fuzzy systems are models that incorporate the learning ability and performance of Artificial Neural Networks (ANNs) with the interpretable reasoning of fuzzy inference systems (FISs). An ANN can learn patterns from data and achieve high accuracy, while a FIS uses linguistic and interpretable rules to match inputs and outputs of the data. Two types of FISs are used the most in literature: Takagi-Sugeno-Kang (TSK) and Mamdani. The main focus of this paper is on the Mamdani neuro-fuzzy systems, notably the Hybrid Neuro-Fuzzy Inference System (HyFIS) and the Neuro-Fuzzy Classifier (NEFCLASS). It aims at evaluating and comparing the two classifiers over two medical datasets to study their performance-interpretability tradeoff. Results show that HyFIS is the best in terms of performance, while NEFCLASS is better in terms of interpretability. As for the performance-interpretability tradeoff, NEFCLASS has the best overall results; it achieves a good performance while being less complicated and more interpretable.

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Acknowledgment

This work was conducted under the research project “Machine Learning based Breast Cancer Diagnosis and Treatment”, 2020–2023. The authors would like to thank the Moroccan Ministry of Higher Education and Scientific Research, Digital Development Agency (ADD), CNRST, and UM6P for their support.

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Authors and Affiliations

  1. MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Hafsaa Ouifak & Ali Idri

  2. Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco

    Ali Idri, Hicham Benbriqa & Ibtissam Abnane

Authors
  1. Hafsaa Ouifak
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  2. Ali Idri
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  3. Hicham Benbriqa
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  4. Ibtissam Abnane
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Corresponding author

Correspondence to Ali Idri .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Ouifak, H., Idri, A., Benbriqa, H., Abnane, I. (2022). Performance-Interpretability Tradeoff of Mamdani Neuro-Fuzzy Classifiers for Medical Data. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_46

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