Enumerative combinatorics
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Nomograms for visualizing support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Strategies for improving the modeling and interpretability of Bayesian networks
Data & Knowledge Engineering
Explaining Classifications For Individual Instances
IEEE Transactions on Knowledge and Data Engineering
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Visual explanation of evidence in additive classifiers
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
A new outlook on Shannon's information measures
IEEE Transactions on Information Theory
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
Modeling the evolution of associated data
Data & Knowledge Engineering
Rough sets based association rules application for knowledge-based system design
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Efficiently explaining decisions of probabilistic RBF classification networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Quality of classification explanations with PRBF
Neurocomputing
Explaining data-driven document classifications
MIS Quarterly
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In this paper, we present a novel method for explaining the decisions of an arbitrary classifier, independent of the type of classifier. The method works at the instance level, decomposing the model's prediction for an instance into the contributions of the attributes' values. We use several artificial data sets and several different types of models to show that the generated explanations reflect the decision-making properties of the explained model and approach the concepts behind the data set as the prediction quality of the model increases. The usefulness of the method is justified by a successful application on a real-world breast cancer recurrence prediction problem.