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A Selective Classifier for Incomplete Data
2008Classifiers based on feature selection (selective classifiers) are a kind of algorithms that can effectively improve the accuracy and efficiency of classification by deleting irrelevant or redundant attributes of a data set. Due to the complexity of processing incomplete data, however, most of them deal with complete data.
Jingnian Chen +3 more
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A Study of Incomplete Data – A Review
2014Incomplete data are questions without answers or variables without observations. Even a small percentage of missing data can cause serious problems with the analysis leading to draw wrong conclusions and imperfect knowledge. There are many techniques to overcome the imperfect knowledge and manage data with incomplete items, but no one is absolutely ...
Sasanko Sekhar Gantayat +2 more
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Concept Lattices of Incomplete Data
2012We present a method of constructing a concept lattice of a formal context with incomplete data. The lattice reduces to a classical concept lattice when the missing values are completed. The lattice also can reflect any known dependencies between the missing values.
Michal Krupka, Jan Lastovicka
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Generating Incomplete Data with DataZapper
2010A nearly universal problem with real data is that they are incomplete, with some values missing. Furthermore, the ways in which values can go missing are quite varied, with arbitrary interdependencies between variables and their values leading to missing values.
Yingying Wen +2 more
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Data Mining with Incomplete Data
2005Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents.
Hai Wang, Shouhong Wang
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Incompleteness of Lamotrigine Data
Drug Safety, 2001N V, Acharya, L V, Wilton, S A, Shakir
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Incomplete Data Decomposition for Classification
2002In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets.
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Incomplete lineage sorting and phenotypic evolution in marsupials
Cell, 2022Guojie Zhang, Duo Xie, Ming Bai
exaly