Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Scaling up inductive learning with massive parallelism
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Estimating concept difficulty with cross entropy
Knowledge discovery and data mining
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In essence, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. The problem is particularly serious because, although each small disjuncts covers few examples, the set of small disjuncts can cover a large number of examples. This paper proposes a solution to the problem of discovering accurate small-disjunct rules based on genetic algorithms. The basic idea of our method is to use a hybrid decision tree / genetic algorithm approach for classification. More precisely, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm, while examples belonging to small disjuncts are classified by a new genetic algorithm, particularly designed for discovering small-disjunct rules.