Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.

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http://dx.doi.org/10.1007/978-3-319-91641-5_16 under the license cc-by
https://academic.microsoft.com/#/detail/2800104830 under the license http://www.springer.com/tdm
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1007/978-3-319-91641-5_16
Licence: CC BY-NC-SA license

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