It is postulated that these rare cases result in small disjuncts in the learned concept. This paper demonstrates that random and systematic attribute noise, class noise, and missing attributes cause the rare cases/small disjuncts to have higher error rates than the common cases/large disjuncts.
Systems that learn from examples often create a disjunctive concept definition. Small disjuncts are those disjuncts which cover only a few.
The experimental results in this paper suggest that for both Shapiro's chess endgame domain and for the Wisconsin breast cancer domain, the assertion that ...
Jul 9, 1995 · Recommendations · Concept learning and the problem of small disjuncts · Handling Small Disjuncts and Class Skew Using Sequential Ellipsoidal ...
Our results suggest that these methods are effective for dealing with class imbalance and, in some cases, might help in ruling out some undesirable disjuncts.
Our results suggest that these methods are eectiv e for dealing with class imbalance and, in some cases, might help in ruling out some un- desirable disjuncts.
First, many concepts include rare or exceptional cases ... (small) set of training examples, selects the disjunct con- ... A case study involving soybean pathology.
enth Belgian-Dutch Conference on Machine Learning, 109-118. Weiss, G. M. (1995). Learning with rare cases and small disjuncts. In Proceedings of the Twelfth ...
cases and small disjuncts to cover rare cases. Concepts with many rare cases are harder to learn than those with few, since general cases can be accurately ...
Small disjuncts are those disjuncts that correctly cover only few training cases ... Moreover, disjuncts induced to cover rare cases ... Weiss, G.M.: Learning with ...