Hierarchical Decision Granules Optimization through the Principle of Justifiable Granularity

Authors

  • Raúl Navarro-Almanza Universidad Autónoma de Baja California
  • Mauricio A. Sanchez Universidad Autónoma de Baja California
  • Juan R. Castro Universidad Autónoma de Baja California
  • Olivia Mendoza Universidad Autónoma de Baja California
  • Guillermo Licea Universidad Autónoma de Baja California

DOI:

https://doi.org/10.13053/cys-26-2-4252

Keywords:

Granular computing, neuro-fuzzy, sugeno, hierarchical decision granules, interpretable machine learning

Abstract

Interpretable Machine Learning (IML) aims to establish more transparent decision processes where the human can understand the reason behind the models’ decisions. In this work a methodology to create intrinsically interpretable models based on fuzzy rules is proposed. There is a selection to identify the rule structure by extracting the most significant elements from a decision tree by the principle of justifiable granularity. There are defined hierarchical decision granules and their quality metrics. The proposal is evaluated with ten publicly available datasets for classification tasks. It is shown that through the principle of justified granularity, rule-based models can be greatly compressed through their fuzzy representation, not only without significantly losing performance but even with compression of 40% it manages to exceed the performance of the initial model.

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Published

2022-06-15

Issue

Vol. 26 No. 2 (2022): Emerging Issues and Applications of Fuzzy Systems, Neural Networks and Metaheuristics (Castillo, Melin, Montiel, Valdez)

Section

Articles of the Thematic Issue

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