Authors:
Sašo Karakatič
;
Marjan Heričko
and
Vili Podgorelec
Affiliation:
University of Maribor, Slovenia
Keyword(s):
Classification, Machine Learning, Genetic Programming, Lazy Evaluation, Dynamic Weighting.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
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.