Inproceedings,
Learning First-order Relations from Noisy Databases using Genetic Algorithms
Proceedings of the Second Singapore International Conference on Intelligent Systems, page B159--164. (1994)
Abstract
In knowledge discovery from databases, we emphasise
the need for learning from huge, incomplete and
imperfect data sets (Piatetsky-Shapiro and Frawley,
1991). To handle noise in the problem domain, existing
learning systems avoid overfitting the imperfect
training examples by excluding insignificant patterns.
The problem is that these systems use a limiting
attribute-value language for representing the training
examples and induced knowledge. Moreover, some
important patterns are ignored because they are
statistically insignificant. This paper describes a
system called GLPS that combines Genetic Algorithms and
a variation of FOIL (Quinlan, 1990) to learn
first-order concepts from noisy training examples. The
performance of GLPS is evaluated on the chess endgame
domain. A detail comparison to FOIL is accomplished and
the performance of GLPS is significantly better than
that of FOIL. This result indicates that the Darwinian
principle of natural selection is a plausible noise
handling method which can avoid overfitting and
identify important patterns at the same time.
