Domain-independent feature extraction for multi-classification using multi-objective genetic programming

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@Article{Zhang:2010:PAA,

• author = "Yang Zhang and Peter Rockett",
• title = "Domain-independent feature extraction for multi-classification using multi-objective genetic programming",
• journal = "Pattern Analysis and Applications",
• year = "2010",
• number = "3",
• volume = "13",
• pages = "273--288",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1433-7541",
• DOI = "doi:10.1007/s10044-009-0154-1",
• size = "16 pages",
• abstract = "We propose three model-free feature extraction approaches for solving the multiple class classification problem; we use multi-objective genetic programming (MOGP) to derive (near-)optimal feature extraction stages as a precursor to classification with a simple and fast-to-train classifier. Statistically-founded comparisons are made between our three proposed approaches and seven conventional classifiers over seven datasets from the UCI Machine Learning database. We also make comparisons with other reported evolutionary computation techniques. On almost all the benchmark datasets, the MOGP approaches give better or identical performance to the best of the conventional methods. Of our proposed MOGP-based algorithms, we conclude that hierarchical feature extraction performs best on multi-classification problems.",
• affiliation = "Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD UK",

}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations