@conference{BarberoECOCOrdinalIWANN,
author = "Javier Barbero-G{\'o}mez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Automatic classification tasks have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs, such as adapting the classic Proportional Odds Model to deep architectures. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. In this work, we present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC) and show how it can improve performance over previously proposed methods.",
booktitle = "2021 International Work-conference on Artificial Neural Networks (IWANN 2021)",
doi = "10.1007/978-3-030-85099-9_1",
isbn = "978-3-030-85029-6",
issn = "0302-9743",
keywords = "Artificial Neural Networks, Ordinal Classification",
month = "16nd-18th June",
number = "Part II",
organization = "Online",
pages = "3-13",
publisher = "Springer",
series = " Lecture Notes in Computer Science (LNCS)",
title = "{E}rror-correcting output codes in the framework of deep ordinal classification",
url = "doi.org/10.1007/978-3-030-85099-9_1",
volume = "12862",
year = "2021",
}