2006 OrdinalRegressionByExtBinClass

Subject Headings: Ordinal Prediction Task.

Notes

It defines an Ordinal Label/Rank as: "ordinal label (i.e., rank) y∈Y = {1, 2, . . ., K}."

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 classifier would then bound the mislabeling cost of the ranking rule. Our framework allows not only to design good ordinal regression algorithms based on well-tuned 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 perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms, which demonstrates the usefulness of our framework.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 OrdinalRegressionByExtBinClass	Ling Li Hsuan-Tien Lin			Ordinal Regression by Extended Binary Classification			http://books.nips.cc/papers/files/nips19/NIPS2006 0880.pdf