This paper proposes an effective bottom-up extension of the popular FIND-S algorithm to learn (monotone) DNF-type rulesets. The algorithm greedily finds a partition of the positive examples. The produced monotone DNF is a set of conjunctive rules, each corresponding to the most specific rule consistent with a part of positive and all negative examples. We also propose two principled extensions of this method, approximating the Bayes Optimal Classifier by aggregating monotone DNF decision rules. Finally, we provide a methodology to improve the explainability of the learned rules while retaining their generalization capabilities. An extensive comparison with state-of-the-art symbolic and statistical methods on several benchmark data sets shows that our proposal provides an excellent balance between explainability and accuracy.

Bayes Point Rule Set Learning

Tommaso Carraro;
2022-01-01

Abstract

This paper proposes an effective bottom-up extension of the popular FIND-S algorithm to learn (monotone) DNF-type rulesets. The algorithm greedily finds a partition of the positive examples. The produced monotone DNF is a set of conjunctive rules, each corresponding to the most specific rule consistent with a part of positive and all negative examples. We also propose two principled extensions of this method, approximating the Bayes Optimal Classifier by aggregating monotone DNF decision rules. Finally, we provide a methodology to improve the explainability of the learned rules while retaining their generalization capabilities. An extensive comparison with state-of-the-art symbolic and statistical methods on several benchmark data sets shows that our proposal provides an excellent balance between explainability and accuracy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/335967
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