Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Behavioral Diversity and a Probabilistically Optimal GP Ensemble
Genetic Programming and Evolvable Machines
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Mining distributed evolving data streams using fractal GP ensembles
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Evolutionary optimization of flavors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Genetically evolved trees representing ensembles
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Lazy meta-learning: creating customized model ensembles on demand
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Cloud driven design of a distributed genetic programming platform
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Hi-index | 0.00 |
In this paper we examine the challenge of producing ensembles of regression models for large datasets. We generate numerous regression models by concurrently executing multiple independent instances of a genetic programming learner. Each instance may be configured with different parameters and a different subset of the training data. Several strategies for fusing predictions from multiple regression models are compared. To overcome the small memory size of each instance, we challenge our framework to learn from small subsets of training data and yet produce a prediction of competitive quality after fusion. This decreases the running time of learning which produces models of good quality in a timely fashion. Finally, we examine the quality of fused predictions over the progress of the computation.