Two-stage learning for multi-class classification using genetic programming

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@Article{Jabeen:2013:Neurocomputing,

• author = "Hajira Jabeen and Abdul Rauf Baig",
• title = "Two-stage learning for multi-class classification using genetic programming",
• journal = "Neurocomputing",
• volume = "116",
• month = "20 " # sep,
• pages = "311--316",
• year = "2013",
• note = "Advanced Theory and Methodology in Intelligent Computing Selected Papers from the Seventh International Conference on Intelligent Computing (ICIC 2011).",
• keywords = "genetic algorithms, genetic programming, Classification, Classifier, Expression, Rule, Algorithm",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2012.01.048",
• URL = "https://hajirajabeen.github.io/publications/NEUCOM.pdf",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231212007308",
• DOI = "doi:10.1016/j.neucom.2012.01.048",
• size = "6 pages",
• abstract = "This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging.",

}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

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