Handling Missing Attribute Values

Jerzy W. Grzymala-Busse () and Witold J. Grzymala-Busse
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Jerzy W. Grzymala-Busse: University of Kansas
Witold J. Grzymala-Busse: TouchNet Information Systems, Inc.

A chapter in Machine Learning for Data Science Handbook, 2023, pp 21-38 from Springer

Abstract: Abstract In this chapter, methods of handling missing attribute values in data mining are described. These methods are categorized into sequential and parallel. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, and then the knowledge is acquired for a data set with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge is acquired directly from the original data sets. In this chapter, the main emphasis is put on rule induction. Methods of handling attribute values for decision tree generation are only briefly summarized.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_2

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DOI: 10.1007/978-3-031-24628-9_2

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