Data mining with genetic algorithms on binary trees
This paper focuses on the automatic interaction detection (AID)-technique, which belongs to the class of decision tree data mining techniques. The AID-technique explains the variance of a dependent variable through an exhaustive and repeated search of all possible relations between the (binary) predictor variables and the dependent variable. This search results in a tree in which non-terminal nodes represent the binary predictor variables, edges represent the possible values of these predictor variables and terminal nodes or leafs correspond to classes of subjects. Despite of being self-evident, the AID-technique has its weaknesses. To overcome these drawbacks a technique is developed that uses a genetic algorithm to find a set of diverse classification trees, all having a large explanatory power. From this set of trees, the data analyst is able to choose the tree that fulfils his requirements and does not suffer from the weaknesses of the AID-technique. The technique developed in this paper uses some specialised genetic operators that are devised to preserve the structure of the trees and to preserve high fitness from being destroyed. An implementation of the algorithm exists and is freely available. Some experiments were performed which show that the algorithm uses an intensification stage to find high-fitness trees. After that, a diversification stage recombines high-fitness building blocks to find a set of diverse solutions.
data mining genetic algorithms binary trees