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Feature selection with mutual information for uncertain data

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Doquire, Gauthier [UCL] Verleysen, Michel [UCL]

In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain data are often encountered, due for example to the imprecision of measurement devices or to continuously moving objects for which the exact position is impossible to obtain. One way to model this uncertainty is to represent each data value as a probability distribution function; recent works show that adequately taking the uncertainty into account generally leads to improved classification performances. Working with such a representation, this paper proposes to achieve feature selection based on mutual information. Experiments on 8 UCI data sets show that the proposed approach is effective to select relevant features.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès restreint
Publication date 2011
Language Anglais
Conference "13th International Conference on Data Warehousing anf Knowledge Discovery (DaWaK 2011)", Toulouse (France) (du 29/08/2011 au 02/09/2011)
Journal information "Lecture Notes in Computer Science" - Vol. 6862, p. 330-341 (2011)
Peer reviewed yes
issn 0302-9743
e-issn 1611-3349
Host document
Publisher Springer-Verlag (Berlin Heidelberg)
Affiliation UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique
Links
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Bibliographic reference Doquire, Gauthier ; Verleysen, Michel. Feature selection with mutual information for uncertain data.13th International Conference on Data Warehousing anf Knowledge Discovery (DaWaK 2011) (Toulouse (France), du 29/08/2011 au 02/09/2011). In: Lecture Notes in Computer Science, Vol. 6862, p. 330-341 (2011)
Permanent URL http://hdl.handle.net/2078.1/116589