Bottom-Up Induction of Feature Terms
Machine Learning
Machine Learning
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
Artificial Intelligence Review
Discovering Plausible Explanations of Carcinogenecity in Chemical Compounds
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Usages of Generalization in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Using explanations for determining carcinogenecity in chemical compounds
Engineering Applications of Artificial Intelligence
Learning from cooperation using justifications
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Classification of melanomas in situ using knowledge discovery with explained case-based reasoning
Artificial Intelligence in Medicine
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In concept learning, inductive techniques perform a global approximation to the target concept. Instead, lazy learning techniques use local approximations to form an implicit global approximation of the target concept. In this paper we present C-LID, a lazy learning technique that uses LID for generating local approximations to the target concept. LID generates local approximations in the form of similitude terms (symbolic descriptions of what is shared by 2 or more cases). C-LID caches and reuses the similitude terms generated in past cases to improve the problem solving of future problems. The outcome of C-LID (and LID) is assessed with experiments on the Toxicology dataset.