Title Evolutionary Extreme Learning Machine for Ordinal Regression
Authors BECERRA ALONSO, DAVID, CARBONERO RUZ, MARIANO, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, MARTÍNEZ ESTUDILLO, ALFONSO CARLOS
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.346
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869000959&doi=10.1007%2f978-3-642-34487-9_27&partnerID=40&md5=a435c93897e0f74c3627f6ca49b0ed3a
Publication date 01/01/2012
ISI 000345089800027
Scopus Id 2-s2.0-84869000959
DOI 10.1007/978-3-642-34487-9_27
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.
Keywords ordinal regression; ordinal classification; extreme learning machine; support vector machine; neural networks
Universidad Loyola members

Change your preferences Manage cookies