RRULES: An improvement of the RULES rule-based classifier

TL;DR

RRULES enhances the RULES rule-based classifier by producing more compact, general rules that improve test accuracy and efficiency, outperforming the original algorithm across multiple datasets.

Contribution

RRULES introduces a more effective rule detection and stopping mechanism, leading to better generalization and faster execution compared to RULES.

Findings

Reduces coverage rate up to 7 times

Runs 2-3 times faster

Achieves higher test accuracy

Abstract

RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more effective mechanism to detect irrelevant rules, at the same time that checks the stopping conditions more often. This results in a more compact rule set containing more general rules which prevent overfitting the training set and obtain a higher test accuracy. Moreover, the results show that RRULES outperforms the original algorithm by reducing the coverage rate up to a factor of 7 while running twice or three times faster consistently over several datasets.