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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Bhowan:2012:SMC,

• author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
• title = "Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data",
• journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
• year = "2012",
• month = apr,
• volume = "42",
• number = "2",
• pages = "406--421",
• size = "16 pages",
• abstract = "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 training criteria in the fitness function. This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data. Using a range of real-world classification problems with class imbalance, we empirically show that these new fitness functions evolve classifiers with good performance on both the minority and majority classes. Our approaches use the original unbalanced training data in the GP learning process, without the need to artificially balance the training examples from the two classes (e.g., via sampling).",
• keywords = "genetic algorithms, genetic programming, GP learning process, biased classifiers, binary classification, class imbalance, data sets, fitness functions, machine learning algorithms, majority class, minority class, training criteria, unbalanced data, unbalanced training data, data handling, learning (artificial intelligence), pattern classification",
• DOI = "doi:10.1109/TSMCB.2011.2167144",
• ISSN = "1083-4419",
• notes = "Also known as \cite{6029340}",

}

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang

Citations