C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Knowledge-based neurocomputing
Knowledge-based neurocomputing
Ensemble learning via negative correlation
Neural Networks
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Using machine learning to prescribe warfarin
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Predicting warfarin dosage from clinical data: A supervised learning approach
Artificial Intelligence in Medicine
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Expert Systems with Applications: An International Journal
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The use of ensembles in machine learning (ML) has had a considerable impact in increasing the accuracy and stability of predictors. This increase in accuracy has come at the cost of comprehensibility as, by definition, an ensemble model is considerably more complex than its component models. This is of significance for decision support systems in medicine because of the reluctance to use models that are essentially black boxes. Work on making ensembles comprehensible has so far focused on global models that mirror the behaviour of the ensemble as closely as possible. With such global models there is a clear tradeoff between comprehensibility and fidelity. In this paper, we pursue another tack, looking at local comprehensibility where the output of the ensemble is explained on a case-by-case basis. We argue that this meets the requirements of medical decision support systems. The approach presented here identifies the ensemble members that best fit the case in question and presents the behaviour of these in explanation.