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2015 | OriginalPaper | Buchkapitel

Creating Effective Error Correcting Output Codes for Multiclass Classification

verfasst von : Wiesław Chmielnicki

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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Abstract

The error correcting output code (ECOC) technique is a genesral framework to solve the multi-class problems using binary classifiers. The key problem in this approach is how to construct the optimal ECOC codewords i.e. the codewords which maximize the recognition ratio of the final classifier. There are several methods described in the literature to solve this problem. All these methods try to maximize the minimal Hamming distance between the generated codewords. In this paper we are showing another approach based both on the average Hamming distance and the estimated misclassification error of the binary classifiers.

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Vorheriges Kapitel Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels

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Titel: Creating Effective Error Correcting Output Codes for Multiclass Classification
verfasst von: Wiesław Chmielnicki
Verlag: Springer International Publishing
Buch: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-319-19643-5

Electronic ISBN: 978-3-319-19644-2

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-19644-2_42

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