Evolutionary Decision Tree Induction with Multi-Interval Discretization

Decision trees are one of the widely used machine learning tools with their most important advantage being their comprehensible structure. Many classic algorithms(usually greedy top-down ones) have been developed for constructing decision trees, while in recent years evolutionary algorithms have found their application in thisarea. Discretization is a technique which enables algorithms like decision trees to deal with continuous attributes as wellas discrete attributes. We present an algorithm that combines the process of multi-interval discretization with tree induction, and introduce especially designed geneticprogramming operators for this task. We compared our algorithm with a classic one, namely C4.5. The comparisonresults suggest that our method is capable of producing smaller trees

کلیدواژه ها:

decision tree induction ، evolutionary algorithm ، genetic programming ، multi-interval discretization