A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Bayesian Error-Bars for Belief Net Inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
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
Fixing the program my computer learned: barriers for end users, challenges for the machine
Proceedings of the 14th international conference on Intelligent user interfaces
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
International Journal of Approximate Reasoning
Explaining instance classifications with interactions of subsets of feature values
Data & Knowledge Engineering
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
Toolkit to support intelligibility in context-aware applications
Proceedings of the 12th ACM international conference on Ubiquitous computing
Why-oriented end-user debugging of naive Bayes text classification
ACM Transactions on Interactive Intelligent Systems (TiiS)
Quality of classification explanations with PRBF
Neurocomputing
The Tag Genome: Encoding Community Knowledge to Support Novel Interaction
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
Software provision in smart environment based on fuzzy logic intelligibility
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Weights of evidence for intelligible smart environments
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Intelligent Data Analysis
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Machine-learned classifiers are important components of many data mining and knowledge discovery systems. In several application domains, an explanation of the classifier's reasoning is critical for the classifier's acceptance by the end-user. We describe a framework, ExplainD, for explaining decisions made by classifiers that use additive evidence. ExplainD applies to many widely used classifiers, including linear discriminants and many additive models. We demonstrate our ExplainD framework using implementations of naïve Bayes, linear support vector machine, and logistic regression classifiers on example applications. ExplainD uses a simple graphical explanation of the classification process to provide visualizations of the classifier decisions, visualization of the evidence for those decisions, the capability to speculate on the effect of changes to the data, and the capability, wherever possible, to drill down and audit the source of the evidence. We demonstrate the effectiveness of ExplainD in the context of a deployed web-based system (Proteome Analyst) and using a downloadable Python-based implementation.