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- W2900287961 abstract "Missing values are an unavoidable issue in many real-world datasets. Classification with incomplete data has to be addressed carefully because inadequate treatment often leads to a big classification error. Interval genetic programming (IGP) is an approach to directly use genetic programming to evolve an effective and efficient classifier for incomplete data. This paper proposes a method to improve IGP for classification with incomplete data by integrating IGP with ensemble learning to build a set of classifiers. Experimental results show that the integration of IGP and ensemble learning to evolve a set of classifiers for incomplete data can achieve better accuracy than IGP alone. The proposed method is also more accurate than other common methods for classification with incomplete data." @default.
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- W2900287961 date "2018-01-01" @default.
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- W2900287961 title "Genetic Programming with Interval Functions and Ensemble Learning for Classification with Incomplete Data" @default.
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- W2900287961 doi "https://doi.org/10.1007/978-3-030-03991-2_53" @default.
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