Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
SVM and Graphical Algorithms: A Cooperative Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
Feature Selection via Coalitional Game Theory
Neural Computation
Explaining Classifications For Individual Instances
IEEE Transactions on Knowledge and Data Engineering
Polynomial calculation of the Shapley value based on sampling
Computers and Operations Research
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
Explaining instance classifications with interactions of subsets of feature values
Data & Knowledge Engineering
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
A general method for visualizing and explaining black-box regression models
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Supporting diagnostics of coronary artery disease with neural networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
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
Rule-based estimation of attribute relevance
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Quality of classification explanations with PRBF
Neurocomputing
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
Intelligent Data Analysis
Explaining data-driven document classifications
MIS Quarterly
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We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method's initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and real-world data sets. The results demonstrate that the method is efficient and that the explanations are intuitive and useful.