Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression

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@Article{Chen:2017:ieeeTEC,

• author = "Qi Chen and Mengjie Zhang and Bing Xue",
• title = "Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2017",
• volume = "21",
• number = "5",
• pages = "792--806",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Symbolic Regression",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2017.2683489",
• abstract = "When learning from high-dimensional data for symbolic regression, genetic programming typically could not generalise well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, in genetic programming for high-dimensional symbolic regression, feature selection before learning is seldom considered. In this work, we propose a new feature selection method based on permutation to select features for high dimensional symbolic regression using genetic programming. A set of experiments has been conducted to investigate the performance of the proposed method on the generalisation of genetic programming for high-dimensional symbolic regression. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability.",
• notes = "also known as \cite{7879832}",

}

Genetic Programming entries for Qi Chen Mengjie Zhang Bing Xue

Citations