@conference{Becerra2012,
author = "David Becerra-Alonso and Mariano Carbonero-Ruz and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and Alfonso Carlos Mart{\'i}nez-Estudillo",
abstract = "This paper presents a novel method for generally adapting ordinal classification models. We essentially rely on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. Under this assumption, this paper proposes an algorithm in two phases that takes advantage of the ordinal structure of the dataset and tries to translate this ordinal structure in the total ordered real line and then to rank the patterns of the dataset. The first phase makes a projection of the ordinal structure of the feature space. Next, an evolutionary algorithm tunes the first projection working with the misclassified patterns near the border of their right class. The results obtained in seven ordinal datasets are competitive in comparison with state-of-the-art algorithms in ordinal regression, but with much less computational time in datasets with many patterns.",
address = "Doha, Qatar",
booktitle = "Proceedings of the 19th International Conference on Neural Information Processing (ICONIP 2012)",
doi = "10.1007/978-3-642-34487-9_27",
editor = "Tingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung",
isbn = "978-3-642-34486-2",
issn = "0302-9743",
note = "November 12-15",
number = "Part III",
pages = "217-227 ",
series = "Lecture Notes In Computer Science",
title = "{E}volutionary {E}xtreme {L}earning {M}achine for {O}rdinal {R}egression",
url = "http://dx.doi.org/10.1007/978-3-642-34487-9_27",
volume = "7665",
year = "2012",
}