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Weighting of Features by Sequential Selection

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Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

Abstract

Constructing a set with characteristic features for supervised classification is a task which can be considered as preliminary for the intended purpose, just a step to take on the way, yet with its significance and bearing on the outcome, the level of difficulty and computational costs involved, the problem has evolved in time to constitute by itself a field of intense study. We can use statistics, available expert domain knowledge, specialised procedures, analyse the set of all accessible features and reduce them backward, we can examine them one by one and select them forward. The process of sequential selection can be conditioned by the performance of a classification system, while exploiting a wrapper model, and the observations with respect to selected variables can result in assignment of weights and ranking. The chapter illustrates weighting of features with the procedures of sequential backward and forward selection for rule and connectionist classifiers employed in the stylometric task of authorship attribution.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Urszula Stańczyk

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  1. Urszula Stańczyk
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Correspondence to Urszula Stańczyk .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Stańczyk, U. (2015). Weighting of Features by Sequential Selection. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_5

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_5

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  • Online ISBN: 978-3-662-45620-0

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