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- W2376835488 abstract "To improve the scalability and adaptability of traditional decision tree learning algorithm,a novel multiple subclassifier integration method of decision forest is proposed based on general information theory.It adopts down-top learning strategy and combines discretization with logical representation of decision tree naturally.The learning procedure does not require any human intervention.The number and structures of subtrees can be set automatically.Experimental results and instance analysis on UCI machine learning data sets prove the feasibility and effectiveness of the proposed method." @default.
- W2376835488 created "2016-06-24" @default.
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- W2376835488 date "2009-01-01" @default.
- W2376835488 modified "2023-09-23" @default.
- W2376835488 title "Multiple Subclassifier Integration Method of Decision Forest Based on General Information Theory" @default.
- W2376835488 hasPublicationYear "2009" @default.
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