Optimising the attribute order in Fuzzy Rough Rule Induction
This work addresses interpretability in machine learning by refining rule induction methods, but it is incremental as it builds on prior FRRI research with limited performance gains.
The paper investigated whether optimizing the attribute order in the FRRI rule induction algorithm improves performance, finding that order optimization alone does not help, but removing a few attributes via fuzzy rough feature selection enhances balanced accuracy and reduces average rule length.
Interpretability is the next pivotal frontier in machine learning research. In the pursuit of glass box models - as opposed to black box models, like random forests or neural networks - rule induction algorithms are a logical and promising avenue, as the rules can easily be understood by humans. In our previous work, we introduced FRRI, a novel rule induction algorithm based on fuzzy rough set theory. We demonstrated experimentally that FRRI outperformed other rule induction methods with regards to accuracy and number of rules. FRRI leverages a fuzzy indiscernibility relation to partition the data space into fuzzy granules, which are then combined into a minimal covering set of rules. This indiscernibility relation is constructed by removing attributes from rules in a greedy way. This raises the question: does the order of the attributes matter? In this paper, we show that optimising only the order of attributes using known methods from fuzzy rough set theory and classical machine learning does not improve the performance of FRRI on multiple metrics. However, removing a small number of attributes using fuzzy rough feature selection during this step positively affects balanced accuracy and the average rule length.