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Classification with Incomplete Data
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Author(s): Pedro J. García-Laencina (Universidad Politécnica de Cartagena, Spain), Juan Morales-Sánchez (Universidad Politécnica de Cartagena, Spain), Rafael Verdú-Monedero (Universidad Politécnica de Cartagena, Spain), Jorge Larrey-Ruiz (Universidad Politécnica de Cartagena, Spain), José-Luis Sancho-Gómez (Universidad Politécnica de Cartagena, Spain)and Aníbal R. Figueiras-Vidal (Universidad Carlos III de Madrid, Spain)
Copyright: 2010
Pages: 29
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch007
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Abstract
Many real-word classification scenarios suffer a common drawback: missing, or incomplete, data. The ability of missing data handling has become a fundamental requirement for pattern classification because the absence of certain values for relevant data attributes can seriously affect the accuracy of classification results. This chapter focuses on incomplete pattern classification. The research works on this topic currently grows wider and it is well known how useful and efficient are most of the solutions based on machine learning. This chapter analyzes the most popular and proper missing data techniques based on machine learning for solving pattern classification tasks, trying to highlight their advantages and disadvantages.
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