Using Entropy to Impute Missing Data in a Classification Task

Thomas Delavallade 1 Thanh Ha Dang 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In real applications, part of the data is usually missing. But most techniques of data analysis and data mining can only deal with complete data. In this paper, a new taxonomy of imputation methods is proposed. Within this taxonomy a new technique, based on entropy measures is introduced. Its behaviour is studied through an empirical comparative analysis.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01306267
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Submitted on : Friday, April 22, 2016 - 4:13:17 PM
Last modification on : Thursday, March 21, 2019 - 1:19:18 PM

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Thomas Delavallade, Thanh Ha Dang. Using Entropy to Impute Missing Data in a Classification Task. IEEE International Conference on Fuzzy Systems (Fuzz-IEEE), Jul 2007, London, United Kingdom. pp.577-582, ⟨10.1109/FUZZY.2007.4295430⟩. ⟨hal-01306267⟩

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