Error-Correcting Output Codes Library. In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Park, Sang-Hyeun; Fürnkranz, Johannes: Efficient implementation of class-based decomposition schemes for naïve Bayes (2014)
- Zhong, Guoqiang; Liu, Cheng-Lin: Error-correcting output codes based ensemble feature extraction (2013)
- Zhou, Jin Deng; Wang, Xiao Dan; Song, Heng: Research on the unbiased probability estimation of error-correcting output coding (2011)