Differentiating between individual class performance in Genetic Programming fitness for classification with unbalanced data
- 1 May 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2802-2809
- https://doi.org/10.1109/cec.2009.4983294
Abstract
This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes.Keywords
This publication has 17 references indexed in Scilit:
- Fitness Functions in Genetic Programming for Classification with Unbalanced DataPublished by Springer Science and Business Media LLC ,2007
- Advanced Genetic Programming Based Machine LearningJournal of Mathematical Modelling and Algorithms, 2007
- A Genetic Programming Classifier Design Approach for Cell ImagesLecture Notes in Computer Science, 2007
- Training Genetic Programming on Half a Million Patterns: An Example From Anomaly DetectionIEEE Transactions on Evolutionary Computation, 2005
- EditorialACM SIGKDD Explorations Newsletter, 2004
- Strategies for learning in class imbalance problemsPattern Recognition, 2003
- Ensemble Methods in Machine LearningLecture Notes in Computer Science, 2000
- Target detection in SAR imagery by genetic programmingAdvances in Engineering Software, 1999
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997
- Bagging predictorsMachine Learning, 1996