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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/119121
Generalized multi-scale stacked sequential learning for multi-class classification
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In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.
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PUERTAS I PRATS, Eloi, ESCALERA GUERRERO, Sergio and PUJOL VILA, Oriol. Generalized multi-scale stacked sequential learning for multi-class classification. Pattern Analysis and Applications. 2015. Vol. 18, num. 2, pags. 247-261. ISSN 1433-7541. [consulted: 13 of October of 2026]. Available at: https://hdl.handle.net/2445/119121