Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Theory Refinement of Bayesian Networks with Hidden Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining Symbolic and Connectionist Learning Methods to RefineCertainty-Factor Rule-Bases
Combining Symbolic and Connectionist Learning Methods to RefineCertainty-Factor Rule-Bases
On the informativeness of the DNA promoter sequences domain theory
Journal of Artificial Intelligence Research
Rerepresenting and restructuring domain theories: a constructive induction approach
Journal of Artificial Intelligence Research
Flexibly exploiting prior knowledge in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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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.This idea can be generalized to a numerical notion of “degree of provedness” which measures the robustness of proofs or refutations for a given instance. This “degree of provedness” can be easily computed using a “soft” interpretation of the theory. Given a ranking of instances based on the values so obtained, all that is required to classify instances is to determine some cutoff threshold above which instances are classified as positive. Such a threshold can be determined on the basis of a small set of training examples.For theories with a few localized flaws, we improve the method by “rehardening”: interpreting only parts of the theory softly, while interpreting the rest of the theory in the usual manner. Isolating those parts of the theory that should be interpreted softly can be done on the basis of a small number of training examples.Softening, with or without rehardening, can be used by itself as a quick way of handling theories with suspected flaws where few training examples are available. Additionally softening and rehardening can be used in conjunction with other methods as a meta-algorithm for determining which theory revision methods are appropriate for a given theory.