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Artificial Intelligence Review

, Volume 25, Issue 3, pp 231–246 | Cite as

Supervised ordering by regression combined with Thurstone’s model

  • Toshihiro Kamishima
  • Shotaro Akaho
Article

Abstract

In this paper, we advocate a learning task that deals with the orders of objects, which we call the Supervised Ordering task. The term order means a sequence of objects sorted according to a specific property, such as preference, size, cost. The aim of this task is to acquire the rule that is used for estimating an appropriate order of a given unordered object set. The rule is acquired from sample orders consisting of objects represented by attribute vectors. Developing solution methods for accomplishing this task would be useful, for example, in carrying out a questionnaire survey to predict one’s preferences. We develop a solution method based on a regression technique imposing a Thurstone’s model and evaluate the performance and characteristics of these methods based on the experimental results of tests using both artificial data and real data.

Keywords

Order Rank Supervised learning Regression analysis Thurstone’s law of comparative judgment Sensory survey 

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Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)Tsukuba, IbarakiJapan

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