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Feature selection on probabilistic symbolic objects

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Abstract

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis (SDA) which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects (PSOs), with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.

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Authors and Affiliations

  1. Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Djamal Ziani

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  1. Djamal Ziani
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Correspondence to Djamal Ziani.

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Djamal Ziani is an assistant professor in Computer Sciences and Information Systems College, King Saud University Saudi Arabia from 2009 until now. He is a researcher in ERP and in data management group of CCIS, King Saud University. He received his MS degree in computer sciences from University of Valenciennes, France in 1992, and his PhD degree in computer sciences from University of Paris Dauphine, France in 1996. Researcher in CLOREC project, INRIA Rocquencourt, France from 1992 to 1996. Post Doc in Department of Computer Sciences and Operational Research of University of Montreal, Canada from 1997 to 1998. Consultant and project manager in many companies in Canada from 1998 to 2009.

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Ziani, D. Feature selection on probabilistic symbolic objects. Front. Comput. Sci. 8, 933–947 (2014). https://doi.org/10.1007/s11704-014-3359-4

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  • DOI: https://doi.org/10.1007/s11704-014-3359-4

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