Istituto di Scienza e Tecnologie dell'Informazione     
Baccianella S., Esuli A., Sebastiani F. Feature selection for ordinal regression. In: SAC 2010 - 25th Symposium on Applied Computing (Sierre, Switzerland, 22-26 marzo 2010). Proceedings, pp. 1748 - 1754. ACM, 2010.
Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. However, while feature selection has been extensively studied for other classification tasks, is has not for ordinal classification. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal classification, and test them on two datasets of product review data.
URL: http://portal.acm.org/browse_dl.cfm?linked=1&part=series&idx=SERIES179&coll=ACM&dl=ACM&CFID=84499686&CFTOKEN=25224284
Subject Ordinal regression
Ordinal classification
Feature selection
I.2.6 Learning (K.3.2)
I.5.2 Design Methodology. Classifier design and evaluation

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