A Multi-Population Genetic Algorithm for Inducing Balanced Decision Trees on Telecommunications Churn Data

In this paper we present a new approach to predicting telecommunications churn. Churn prediction can be considered as a multi-objective optimization problem, where the accuracy of predicting both churning and staying consumers need to be optimized simultaneously. As the existing classification methods failed to produce balanced solutions, we developed a new multi-population genetic algorithm for the induction of decision trees. By introducing multiple populations, linear ranking selection and adequate fitness function we were able to avoid overly biased solutions. The evaluation results of our algorithm's performance in comparison with the existing methods show that it was able to find highly accurate and balanced solutions.