Dealing with heterogeneity in the context of distributed feature selection for classification
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Dealing with heterogeneity in the context of distributed feature selection for classificationData
2021Cita bibliográfica
Morillo-Salas, J.L., Bolón-Canedo, V. & Alonso-Betanzos, A. Dealing with heterogeneity in the context of distributed feature selection for classification. Knowl Inf Syst 63, 233–276 (2021). https://doi.org/10.1007/s10115-020-01526-4
Resumo
[Abstract]: Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be handled in a centralized manner. A possible solution to this problem is to distribute the data over several processors and combine the different results. We propose a methodology to distribute feature selection processes based on selecting relevant and discarding irrelevant features. This preprocessing step is essential for current high-dimensional sets, since it allows the input dimension to be reduced. We pay particular attention to the problem of data imbalance, which occurs because the original dataset is unbalanced or because the dataset becomes unbalanced after data partitioning. Most works approach unbalanced scenarios by oversampling, while our proposal tests both over- and undersampling strategies. Experimental results demonstrate that our distributed approach to classification obtains comparable accuracy results to a centralized approach, while reducing computational time and efficiently dealing with data imbalance.
Palabras chave
Feature selection
Distributed learning
Unbalanced data
Oversampling
Distributed learning
Unbalanced data
Oversampling
Descrición
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10115-020-01526-4.
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ISSN
0219-1377
0219-3116
0219-3116