NDLI: AdaBoost regression algorithm based on classification-type loss

Content Provider	IEEE Xplore Digital Library
Author	Lin Gao Peng Kou Feng Gao Xiaohong Guan
Copyright Year	2010
Description	Author affiliation: School of Electrical Engineering, Xi'an Jiaotong University, Shaanxi Province, China (Lin Gao) \|\| SKLMS, Systems Engineering Institute, Xi'an Jiaotong University, Shaanxi Province, China (Peng Kou; Feng Gao; Xiaohong Guan)
Abstract	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.
Starting Page	682
Ending Page	687
File Size	1152039
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554029
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Boosting Training Support vector machines Prediction algorithms Convergence Fitting support vector regression boosting algorithm ensemble learning regression estimation
Content Type	Text
Resource Type	Article

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