C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
From Ensemble Methods to Comprehensible Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
ECML '07 Proceedings of the 18th European conference on Machine Learning
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Ensemble methods are popular learning methods that are usually able to increase the predictive accuracy of a classifier. On the other hand, this comes at the cost of interpretability, and insight in the decision process of an ensemble is hard to obtain. This is a major reason why ensemble methods have not been extensively used in the setting of inductive logic programming. In this paper we aim to overcome this issue of comprehensibility by learning a single first order interpretable model that approximates the first order ensemble. The new model is obtained by exploiting the class distributions predicted by the ensemble. These are employed to compute heuristics for deciding which tests are to be used in the new model. As such we obtain a model that is able to give insight in the decision process of the ensemble, while being more accurate than the single model directly learned on the data.