Maximizing Theory Accuracy Through Selective Reinterpretation

摘要

Existing methods for exploiting flawed domain theories depend on the use of a sufficiently large set of training examples for diagnosing and repairing flaws in the theory. In this paper, we offer a method of theory reinterpretation that makes only marginal use of training examples. The idea is as follows: Often a small number of flaws in a theory can completely destroy the theory's classification accuracy. Yet it is clear that valuable information is available even from such flawed theories. For example, an instance with severalindependent proofs in a slightly flawed theory is certainly more likely to be correctly classified as positive than an instance with only a single proof.