Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The class imbalance problem: A systematic study
Intelligent Data Analysis
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Multi-Objective Genetic Programming for Classification with Unbalanced Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
GP classification under imbalanced data sets: active sub-sampling and AUC approximation
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this performance is usually better than many of its individual members.