Multinomial Bayesian extreme learning machine for sparse and accurate classification model

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	Multinomial Bayesian extreme learning machine for sparse and accurate classification model
	Luo, J.H.; WONG, C.M.; Vong, C. M.
	2021
Source Publication	Neurocomputing (SCI-E)
ISSN	0925-2312
Pages	24-33
Abstract	Sparse Bayesian extreme learning machine (SBELM) is a probabilistic model with three-layer neural network, which is superior to extreme learning machine (ELM) in model generalization, sparsity and execution time. In SBELM, Bernoulli distribution is employed for binary classification, and then extended to multi-class classification using pairwise coupling. However, pairwise coupling suffers from three significant drawbacks for multi-class classification: 1) classification ambiguity and uncovered class regions; 2) large model size; 3) insufficient uncertainty representation for label prediction in probabilities. To alleviate these drawbacks, multinomial Bayesian extreme learning machine (MBELM) is proposed that employs multinomial distribution, which is proposed for multi-class classification. For the sake of various concerns between sparsity and accuracy, two sparse mechanisms namely automatic relevance determination (ARD) and penalty are respectively integrated with MBELM. The experimental results show that, compared to SBELM, the proposed MBELM improves the test accuracy and the model size respectively up to 5% better, and 94 times smaller for multi-class classification.
Keyword	Extreme learning machine Sparse Bayesian Multi-class classification Sparse learning Multinomial distribution
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	58829
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Vong, C. M.
Recommended Citation GB/T 7714	Luo, J.H.,WONG, C.M.,Vong, C. M.. Multinomial Bayesian extreme learning machine for sparse and accurate classification model[J]. Neurocomputing (SCI-E), 2021, 24-33.
APA	Luo, J.H.., WONG, C.M.., & Vong, C. M. (2021). Multinomial Bayesian extreme learning machine for sparse and accurate classification model. Neurocomputing (SCI-E), 24-33.
MLA	Luo, J.H.,et al."Multinomial Bayesian extreme learning machine for sparse and accurate classification model".Neurocomputing (SCI-E) (2021):24-33.

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