Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Learning and example selection for object and pattern detection
Learning and example selection for object and pattern detection
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolving ensembles in multi-objective genetic programming for classification with unbalanced data
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Genetic programming for classification with unbalanced data
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
A measure oriented training scheme for imbalanced classification problems
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Improving robustness of multiple-objective genetic programming for object detection
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.