A generic optimising feature extraction method using multiobjective genetic programming

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@Article{Zhang20111087,

• author = "Yang Zhang and Peter I. Rockett",
• title = "A generic optimising feature extraction method using multiobjective genetic programming",
• journal = "Applied Soft Computing",
• year = "2011",
• volume = "11",
• number = "1",
• pages = "1087--1097",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Feature extraction, Multiobjective optimisation, Pattern recognition",
• ISSN = "1568-4946",
• broken = "http://www.sciencedirect.com/science/article/B6W86-4YGHGKT-2/2/3c6f14d2e029af14747957a5a2ccfd11",
• DOI = "doi:10.1016/j.asoc.2010.02.008",
• abstract = "In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem.",

}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations