Bottom-Up Induction of Feature Terms
Machine Learning
Lazy Induction of Descriptions for Relational Case-Based Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
Using symbolic descriptions to explain similarity on CBR
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Remembering similitude terms in CBR
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
An ontological approach to represent molecular structure information
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
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
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The goal of predictive toxicology is the automatic construction of carcinogenecity models. Most common artificial intelligence techniques used to construct these models are inductive learning methods. In a previous work we presented an approach that uses lazy learning methods for solving the problem of predicting carcinogenecity. Lazy learning methods solve new problems based on their similarity to already solved problems. Nevertheless, a weakness of these kind of methods is that sometimes the result is not completely understandable by the user. In this paper we propose an explanation scheme for a concrete lazy learning method. This scheme is particularly interesting to justify the predictions about the carcinogenesis of chemical compounds.