Original Contribution: Stacked generalization
Neural Networks
Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Local Reinforcement and Recombination in Classifier Systems
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
MOGE: GP classification problem decomposition using multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
Novel ways of improving cooperation and performance in ensemble classifiers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Ensemble learning for free with evolutionary algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
New methods for competitive coevolution
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GP on SPMD parallel graphics hardware for mega Bioinformatics data mining
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
GP classification under imbalanced data sets: active sub-sampling and AUC approximation
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Cooperative problem decomposition in Pareto competitive classifier models of coevolution
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A hierarchical cooperative evolutionary algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
Symbiogenesis as a mechanism for building complex adaptive systems: a review
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Autonomous Virulence Adaptation Improves Coevolutionary Optimization
IEEE Transactions on Evolutionary Computation
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On the utility of trading criteria based retraining in forex markets
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
On GPU based fitness evaluation with decoupled training partition cardinality
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Malicious automatically generated domain name detection using Stateful-SBB
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class--each with a potentially unique attribute subspace--without recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to separate the tasks of team/ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken from the UCI repository with 649---102,660 attributes and 2---10 classes. The resulting teams identify attribute spaces 1---4 orders of magnitude smaller than under the original data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.