Genetic algorithm based multiple decision tree induction

There are two fundamental weaknesses which may have a great impact on the performance of decision tree (DT) induction. These are the limitations in the ability of the DT language to represent some of the underlying patterns of the domain and the degradation in the quality of evidence available to the induction process caused by its recursive partitioning of the training data. The impact of these two weaknesses is greatest when the induction process attempts to overcome the first weakness by resorting to more partitioning of the training data, thus increasing its vulnerability to the second weakness. The authors investigate the use of multiple DT models as a method of overcoming the limitations of the DT modeling language and describe a new and novel algorithm to automatically generate multiple DT models from the same training data. The algorithm is compared to a single-tree classifier by experiments on two well known data sets. Results clearly demonstrate the superiority of our algorithm