Concept learning and heuristic classification in weak-theory domains
Artificial Intelligence
A Nearest Hyperrectangle Learning Method
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Automatic feature generation for problem solving systems
ML92 Proceedings of the ninth international workshop on Machine learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Computing Optimal Attribute Weight Settings for Nearest NeighborAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Explanation-Driven Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Combining CBR with Interactive Knowledge Acquisition, Manipulation and Reuse
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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This paper proposes to enhance similarity-based classification with different types of imperfect domain knowledge. We introduce a hierarchy of knowledge types and show how the types can be incorporated into similarity measures. Furthermore, we analyze how properties of the domain theory, such as partialness and vagueness, influence classification accuracy. Experiments in a simple domain suggest that partial knowledge is more useful than vague knowledge. However, for data sets from the UCI Machine Learning Repository, we show that even vague domain knowledge that in isolation performs at chance level can substantially increase classification accuracy when being incorporated into similarity-based classification.