期刊名称：International Journal of Computer Systems Science and Engineering
印刷版ISSN：1307-430X
出版年度：2007
卷号：03
期号：01
页码：7-7
出版社：World Academy of Science, Engineering and Technology
摘要：Instead of traditional (nominal) classification we investigate
the subject of ordinal classification or ranking. An enhanced
method based on an ensemble of Support Vector Machines (SVM¡¯s)
is proposed. Each binary classifier is trained with specific weights
for each object in the training data set. Experiments on benchmark
datasets and synthetic data indicate that the performance of our
approach is comparable to state of the art kernel methods for
ordinal regression. The ensemble method, which is straightforward
to implement, provides a very good sensitivity-specificity trade-off
for the highest and lowest rank.
关键词：Ordinal regression, support vector machines, ensemble
learning.
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