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Missing Attribute Values

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Encyclopedia of Machine Learning and Data Mining

Synonyms

Missing values; Unknown attribute values; Unknown values

Definition

When inducing decision trees or decision rules from real-world data, many different aspects must be taken into account. One important aspect, in particular, is the processing of missing (unknown) attribute values. In machine learning (ML), instances (objects, observations) are usually represented by a list of attribute values; such a list commonly has a fixed length (i.e., a fixed number of attributes).

The topic of missing attribute values has been analyzed in the field of ML in many papers (Brazdil and Bruha 1992; Bruha and Franek 1996; Karmaker and Kwer 2005; Long and Zhang 2004; Quinlan 1986, 1989). Grzymala-Busse (2003) and Li and Cercone (2006) discuss the treatment of missing attribute values using the rough set strategies.

There are a few directions in which missing (unknown) attribute values as well as the corresponding routines for their processing may be studied and designed. First, the source of...

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Recommended Reading

  • Brazdil PB, Bruha I (1992) A note on processing missing attribute values: a modified technique. In: Workshop on machine learning, Canadian conference AI, Vancouver

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  • Bruha I (2003) Unknown attribute value processing by domain-specific external expert knowledge. In: 7th WSEAS international conference on systems, Corfu

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  • Bruha I (2004) Meta-learner for unknown attribute values processing: dealing with inconsistency of meta-databases. J Intell Inf Syst 22(1):71–84

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Author information

Authors and Affiliations

  1. McMaster University, Hamilton, ON, Canada

    Ivan Bruha

Authors
  1. Ivan Bruha
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Bruha, I. (2017). Missing Attribute Values. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_954

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