Feature selection with limited bit depth mutual information for portable embedded systems
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http://hdl.handle.net/2183/36406
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- GI-LIDIA - Artigos [54]
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Feature selection with limited bit depth mutual information for portable embedded systemsAutor(es)
Data
2020-06Cita bibliográfica
Moran-Fernandez, L., Sechidis, K., Bolon-Canedo, V., Alonso-Betanzos, A., & Brown, G. (2020). Feature selection with limited bit depth mutual information for portable embedded systems. Knowledge-Based Systems, 197, 105885. https://doi.org/10.1016/j.knosys.2020.105885
Resumo
[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. This work considers mutual information – one of the most common measures of dependence used in feature selection algorithms – with a limited number of bits. In order to test the procedure designed, we have implemented it in several well-known feature selection algorithms. Experimental results over several synthetic and real datasets demonstrate that low bit representations are sufficient to achieve performances close to that of double precision parameters and thus open the door for the use of feature selection in embedded platforms that minimize the energy consumption and carbon emissions.
Palabras chave
Reduced precision
Mutual information
Feature selection
Portable embedded systems
Internet of things
Edge computing
Mutual information
Feature selection
Portable embedded systems
Internet of things
Edge computing
Descrición
This version of the article:
Morán-Fernández, L., Sechidis, K., Bolón-Canedo, V., Alonso-Betanzos, A., & Brown, G.
(2020). ‘Feature selection with limited bit depth mutual information for portable
embedded systems’ has been accepted for publication in: Knowledge-Based Systems, 197,
105885. The Version of Record is available online at
https://doi.org/10.1016/j.knosys.2020.105885.
Versión do editor
Dereitos
Atribución-NoComercial-SinDerivadas 4.0 Internacional
ISSN
0950-7051
1872-7409
1872-7409