Experiments with Lazy Evaluation of Classification Decision Trees Made with Genetic Programming

摘要

In this paper, we present a lazy evaluation approach of classification decision trees with genetic programming. We describe and experiment with the lazy evaluation that does not evaluate the whole population but evaluates only the individuals that are chosen to participate in the tournament selection method. Further on, we used dynamic weights for the classification instances, that are linked to the chance of that instance getting picked for the evaluation process. These weights change based on the misclassification rate of the instance. We test our lazy evaluation approach on 10 standard classification benchmark datasets and show that not only lazy evaluation approach uses less time to evolve the good solution, but can even produce better solution due to changing instance weights and thus preventing the overfitting of the solutions.