Advanced Experiments for Learning Qualitative Compartment Models
نویسندگان
چکیده
In this paper, the learning of qualitative twocompartment metabolic models is studied under the conditions of different types and numbers of hidden variables. For each condition, all the experiments, each of which takes one of the subsets of the complete qualitative states as training data, are tested one by one. In order to conduct the experiments more efficiently, a backtracking algorithm with forward checking is introduced to search out all the well-posed qualitative models as candidate solutions. Then these candidate solutions are verified by a fuzzy qualitative engine JMorven to find the target models. Finally the learning reliability and kernel set under different conditions is calculated and analyzed.
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