Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data

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@Article{Al-Helali:EC,

• author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang",
• title = "Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data",
• journal = "Evolutionary Computation",
• month = nov # " 21 2024",
• note = "Just Accepted",
• keywords = "genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Feature Selection, High-dimensionality",
• ISSN = "1063-6560",
• DOI = "10.1162/evco_a_00362",
• abstract = "High-dimensionality is one of the serious real-world data challenges in symbolic regression and it is more challenging if the data are incomplete. ... a genetic programming-based approach to select features directly from incomplete high-dimensional data to improve symbolic regression performance. We extend the concept of identity/neutral elements from mathematics into the function operators of genetic programming, thus they can handle the missing values in incomplete data. Experiments have been conducted on a number of data sets considering different missingness ratios in high-dimensional symbolic regression tasks. The results show that the proposed method leads to better symbolic regression results when compared with state-of-the-art methods that can select features directly from incomplete data. Further results show that our approach not only leads to better symbolic regression accuracy but also selects a smaller number of relevant features ...",

}

Genetic Programming entries for Baligh Al-Helali Qi Chen Bing Xue Mengjie Zhang

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