Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
Machine Learning
Machine Learning
Machine Learning
From Ensemble Methods to Comprehensible Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Relational Learning Using Constrained Confidence-Rated Boosting
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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 a first order ensemble
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the decision process. In this paper we aim to overcome this issue of comprehensibility by learning a single decision tree that approximates an ensemble of decision trees. 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 tree. As such we acquire a model that is able to give insight in the decision process, while being more accurate than the single model directly learned on the data. The proposed method is experimentally evaluated on a large number of UCI data sets, and compared to an existing approach that makes use of artificially generated data.