Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data

@article{Bhowan2012DevelopingNF,
  title={Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data},
  author={Urvesh Bhowan and Mark Johnston and Mengjie Zhang},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2012},
  volume={42},
  pages={406-421}
}
Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class has on traditional… 

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