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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