 
Summary: Ordinal Regression by Extended Binary Classification
Ling Li
Learning Systems Group
California Institute of Technology
ling@caltech.edu
HsuanTien Lin
Learning Systems Group
California Institute of Technology
htlin@caltech.edu
Abstract
We present a reduction framework from ordinal regression to binary classification
based on extended examples. The framework consists of three steps: extracting
extended examples from the original examples, learning a binary classifier on the
extended examples with any binary classification algorithm, and constructing a
ranking rule from the binary classifier. A weighted 0/1 loss of the binary classi
fier would then bound the mislabeling cost of the ranking rule. Our framework
allows not only to design good ordinal regression algorithms based on welltuned
binary classification approaches, but also to derive new generalization bounds for
ordinal regression from known bounds for binary classification. In addition, our
framework unifies many existing ordinal regression algorithms, such as percep
