NDLI: An adaptive multiclass boosting algorithm for classification

Content Provider	IEEE Xplore Digital Library
Author	Shixun Wang Peng Pan Yansheng Lu
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Shixun Wang; Peng Pan; Yansheng Lu)
Abstract	A large number of practical domains, such as scene classification and object recognition, have involved more than two classes. Therefore, how to directly conduct multiclass classification is being an important problem. Although some multiclass boosting methods have been proposed to deal with the problem, the combinations of weak learners are confined to linear operation, namely weighted sum. In this paper, we present a novel large-margin loss function to directly design multiclass classifier. The resulting risk, which guarantees Bayes consistency and global optimization, is minimized by gradient descent or Newton method in a multidimensional functional space. At every iteration, the proposed boosting algorithm adds the best weak learner to the current ensemble according to the corresponding operation that can be sum or Hadamard product. This process grown in an adaptive manner can create the sum of Hadamard products of weak learners, leading to a sophisticated nonlinear combination. Extensive experiments on a number of UCI datasets show that the performance of our method consistently outperforms those of previous multiclass boosting approaches for classification.
Starting Page	1159
Ending Page	1166
File Size	2969782
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889526
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Boosting Classification algorithms Algorithm design and analysis Optimization Newton method Additives Probabilistic logic probabilistic outputs multiclass boosting classification loss function nonlinear combination
Content Type	Text
Resource Type	Article

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