An Overview on Ordinal Regression

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• Dr. Pedro Antonio Gutiérrez, Department of Computer Science and Numerical Analysis, University of Córdoba
• Wednesday 21 September 2011, 16:00-17:00
• UG40, School of Computer Science.

If you have a question about this talk, please contact Leandro Minku.

Ordinal classification (so called ranking, sorting or ordinal regression) is a supervised learning problem of predicting categories of ordinal scale. The samples are labelled by a set of ranks with an ordering among the different categories.

In contrast to the usual classification, there is an ordinal relationship among the categories and it is different from regression in that the number of ranks is finite and the exact amounts of difference among the ranks are not defined. In this way, ordinal classification lies somewhere between classification and regression.

Ordinal classification problems are important, since they are very common in our everyday life where many problems require the classification of items into naturally ordered classes: selecting the best route to work, where to stop, which product to buy, and where to live, are just examples of daily ordinal decision-making.

In this talk, we will try to summarize the main current trends in ordinal regression, presenting and comparing some of the methods that try to improve classification performance under these constraints.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG40, School of Computer Science

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