Title of article :
Using explanations for determining carcinogenecity in chemical compounds
Author/Authors :
Armengol، نويسنده , , Eva، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Abstract :
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. In addition we propose that these explanations could be used to build a partial domain knowledge. In our particular case, we use the explanations for building general knowledge about carcinogenesis.
Keywords :
Predictive toxicology , Lazy learning , Partial domain models , Feature terms , Lazy induction of descriptions , Explanations
Journal title :
Engineering Applications of Artificial Intelligence
Journal title :
Engineering Applications of Artificial Intelligence