Feature selection for ordinal regression

Stefano Baccianella, Andrea Esuli, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)


Ordinal regression (also known as ordinal classification) 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 regression. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal regression, and test them on two datasets of product review data.

Original languageEnglish
Title of host publicationAPPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing
Number of pages7
Publication statusPublished - 23 Jul 2010
Event25th Annual ACM Symposium on Applied Computing, SAC 2010 - Sierre, Switzerland
Duration: 22 Mar 201026 Mar 2010

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference25th Annual ACM Symposium on Applied Computing, SAC 2010


  • feature selection
  • ordinal classification
  • ordinal regression
  • product reviews

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Feature selection for ordinal regression'. Together they form a unique fingerprint.

  • Cite this

    Baccianella, S., Esuli, A., & Sebastiani, F. (2010). Feature selection for ordinal regression. In APPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing (pp. 1748-1754). (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/1774088.1774461