Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A methodology to explain neural network classification
Neural Networks
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
An introduction to variable and feature selection
The Journal of Machine Learning Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
Bioinformatics
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Explaining Classifications For Individual Instances
IEEE Transactions on Knowledge and Data Engineering
Towards a Model Independent Method for Explaining Classification for Individual Instances
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Explaining data-driven document classifications
MIS Quarterly
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After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.