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RRULES: An improvement of the RULES rule-based classifier

2021-06-14 10:42:12
Rafel Palliser-Sans

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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.07296

PDF

https://arxiv.org/pdf/2106.07296.pdf


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