By Topic

AdaBoost regression algorithm based on classification-type loss

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Lin Gao ; Sch. of Electr. Eng., Xi''an Jiaotong Univ., Xi''an, China ; Peng Kou ; Feng Gao ; Xiaohong Guan

This paper presents a new concept of building classification-type loss for regression sample based on conversion between regression and classification problems used in Support Vector Regression (SVR). By introducing the classification-type loss to calculate example's error, AdaBoost algorithm can be generalized from classification to regression. A new Boosting algorithm for regression, called AdaBoost.SVR.R which can be directly applied to a regression problem is proposed. SVR is used as its base learner. Its output is an ensemble of a team of regression functions. The employing of the classification-type loss makes the iterating process of AdaBoost.SVR.R act essentially on a converted binary classification problem. The output scheme of AdaBoost.SVR.R is also derived upon constructing decision function of the binary classification problem. Since it has the same application condition as AdaBoost, AdaBoost.SVR.R could satisfy the convergence proof of AdaBoost algorithm. The testing results for the considered data sets show that the new algorithm is effective.

Published in:

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Date of Conference:

7-9 July 2010