A method for inductive cost optimization
In this paper we present a Method for Inductive Cost Optimization (MICO), as an example of induction biased by using background knowledge. The method produces a decision tree that identifies those setpoints that enable the process to produce in as cost-efficient a manner as possible. We report on two examples, one idealised and one real-world. Some problems concerning MICO are reported.
KeywordsMachine Learning Biased Inductive Learning Cost Optimization Decision Trees
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