A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts
Déborah Ribeiro Carvalho, Alex Alves Freitas · Lecture notes in computer science · 2000
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.