Removing examples and discovering hierarchical decision Rules - With evolutionary algorithms
This paper describes an approach based on evolutionary algorithms, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be therefore tried in order until one is found whose conditions are satisfied. Due to the computational cost of the evolutionary algorithms, we have developed a preprocesing method to reduce the number of examples from the database. This method, named EOP (Editing by Ordered Projections), has some interesting characteristics, especially from the point of view of the application of axis-parallel classifiers. We have tested our system on real data from the UCI Repository, and the results of a 10-fold cross-validation are compared to C4.5's and C4.5Rules'. The experiments showed that HIDER works well in practice.
decision rules evolutionary algorithms supervised learning preprocessing techniques