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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2014, Vol. 8 Issue (6) : 933-947    https://doi.org/10.1007/s11704-014-3359-4
RESEARCH ARTICLE
Feature selection on probabilistic symbolic objects
Djamal ZIANI()
Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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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.

Keywords symbolic data analysis      feature selection      probabilistic symbolic object      discrimination criteria      data and knowledge visualization     
Corresponding Author(s): Djamal ZIANI   
Issue Date: 27 November 2014
 Cite this article:   
Djamal ZIANI. Feature selection on probabilistic symbolic objects[J]. Front. Comput. Sci., 2014, 8(6): 933-947.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3359-4
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I6/933
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