Generalized multi-scale stacked sequential learning for multi-class classification

doi:10.1007/s10044-013-0333-y

Generalized multi-scale stacked sequential learning for multi-class classification

2013-04-19

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DOI：10.1007/s10044-013-0333-y

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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.

Keywords:

Stacked sequential learning

Multi-scale

Error-correct output codes (ECOC)

Contextual classification

Generalized multi-scale stacked sequential learning for multi-class classification

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