On the design of low redundancy error-correcting output codes

Miguel Ángel Bautista, Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers. © 2011 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    Pages (from-to)21-38
    JournalStudies in Computational Intelligence
    Volume373
    DOIs
    Publication statusPublished - 24 Oct 2011

    Fingerprint Dive into the research topics of 'On the design of low redundancy error-correcting output codes'. Together they form a unique fingerprint.

    Cite this