Hierarchical classification trees using type-constrained genetic programming

We investigate the capability of the genetic programming approach for producing hierarchical, rule-based, classification trees. These trees can be seen as an extension to the machine learning decision trees concept, where the predicates here can be complex expressions rather than just simple attribute-value comparisons. In order to improve the search ability and to produce meaningful results, type-constraints are applied to the genetic programming procedure, expressed in a BNF grammar. The model is tested in two well-known domains. In the Balance-Scale data, the system achieves in revealing the data creation rule. In the E-Coli Protein Localization Sites data, the system realizes a competitor to the literature classification score, retaining the solution comprehensibility. The training procedure is guided by an adaptive fitness measure. The overall performance of this system denotes its competitiveness to standard computational intelligent procedures.