Modelling legal argument: reasoning with cases and hypotheticals
Modelling legal argument: reasoning with cases and hypotheticals
Reasoning with cases and hypotheticals in HYPO
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Building a Case-Based Help Desk Application
IEEE Expert: Intelligent Systems and Their Applications
Case-Based Reasoning Technology, From Foundations to Applications
Case Retrieval Nets: Basic Ideas and Extensions
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Omega: on-line memory-based general purpose system classifier
Omega: on-line memory-based general purpose system classifier
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
A Case-Based Explanation System for Black-Box Systems
Artificial Intelligence Review
Reasoning symbolically about partially matched cases
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
An evaluation of the usefulness of case-based explanation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
The best way to instil confidence is by being right
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
A Scalable Noise Reduction Technique for Large Case-Based Systems
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
A natural language argumentation interface for explanation generation in Markov decision processes
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Traditional explanation strategies in machine learning have been dominated by rule and decision tree based approaches. Case-based explanations represent an alternative approach which has inherent advantages in terms of transparency and user acceptability. Case-based explanations are based on a strategy of presenting similar past examples in support of and as justification for recommendations made. The traditional approach to such explanations, of simply supplying the nearest neighbour as an explanation, has been found to have shortcomings. Cases should be selected based on their utility in forming useful explanations. However, the relevance of the explanation case may not be clear to the end user as it is retrieved using domain knowledge which they themselves may not have. In this paper the focus is on a knowledge-light approach to case-based explanations that works by selecting cases based on explanation utility and offering insights into the effects of feature-value differences. In this paper we examine to two such a knowledge-light frameworks for case-based explanation. We look at explanation oriented retrieval (EOR) a strategy which explicitly models explanation utility and also at the knowledge-light explanation framework (KLEF) that uses local logistic regression to support case-based explanation.