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Type: Journal article
Title: RandomBoost: simplified multiclass boosting through randomization
Author: Paisitkriangkrai, S.
Shen, C.
Shi, Q.
van den Hengel, A.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2014; 25(4):764-779
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2014
ISSN: 2162-2388
Statement of
Sakrapee Paisitkriangkrai, Chunhua Shen, Qinfeng Shi, and Anton van den Hengel
Abstract: We propose a novel boosting approach to multiclass classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multiclass classification. The result is a multiclass classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning, and visual recognition data sets demonstrate that our proposed methods could be compared favorably with existing multiclass boosting algorithms in terms of both the convergence rate and classification accuracy.
Rights: © 2013 IEEE.
RMID: 0030007792
DOI: 10.1109/TNNLS.2013.2281214
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Appears in Collections:Computer Science publications

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