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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.
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Keywords
symbolic data analysis
feature selection
probabilistic symbolic object
discrimination criteria
data and knowledge visualization
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Corresponding Author(s):
Djamal ZIANI
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Issue Date: 27 November 2014
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