PT Journal
AU Eloi Puertas
   Sergio Escalera
   Oriol Pujol
TI Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification
SO Pattern Analysis and Applications
JI PAA
PY 2015
BP 247
EP 261
VL 18
IS 2
DI 10.1007/s10044-013-0333-y
DE Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification
AB 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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