Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Data Mining and Knowledge Discovery
Adapting the Fitness Function in GP for Data Mining
Proceedings of the Second European Workshop on Genetic Programming
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multi-Objective Genetic Programming for Classification with Unbalanced Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Fitness functions in genetic programming for classification with unbalanced data
AI'07 Proceedings of the 20th Australian 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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Two-Tier genetic programming: towards raw pixel-based image classification
Expert Systems with Applications: An International Journal
Genetic programming for biomarker detection in mass spectrometry data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods can produce the deceptive appearance of “good looking” results even when classification performance on the important minority class can be poor. This paper compares two Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary classification tasks with unbalanced data.