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A generic optimising feature extraction method using multiobjective genetic programming

A generic optimising feature extraction method using multiobjective genetic programming,10.1016/j.asoc.2010.02.008,Applied Soft Computing,Yang Zhang,P

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A generic optimising feature extraction method using multiobjective genetic programming   (Citations: 2)
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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.
Journal: Applied Soft Computing - ASC , vol. 11, no. 1, pp. 1087-1097, 2011
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    • ...Research on GP feature extraction has obtained performances competitive with other classification algorithms (see [15], for example) but limiting the decision space to a single dimension is a restriction on the feature extraction process...
    • ...We need to articulate a preference to select a single classifier and in common with [2, 15, 25], we have used the error measured over the data fold which was not used for training...

    Khaled Badran, et al. Multi-class pattern classification using single, multi-dimensional fea...

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