HYDRA : a noise-tolerant relational concept learning algorithm

Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the negative training data. This paper presents a method for attaching likelihood ratios to clauses and a method for using such ratios to classify test examples. This paper presents the relational concept learner HYDRA that learns a concept description for each class. Each concept description competes to classify the test example using the likelihood ratios assigned to clauses of that concept description. By testing on several artificial and "real world" domains, we demonstrate that attaching weights and allowing concept descriptions to compete to classify examples reduces an algorithm's susceptibility to noise