Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning

Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. More...

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Published in:	Natural computing Vol. 20; no. 1; pp. 11 - 22
Main Authors:	Cheng, Jian, Chen, Jingjing, Guo, Yi-nan, Cheng, Shi, Yang, Linkai, Zhang, Pei
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.03.2021 Springer Nature B.V
Subjects:	Adaptive algorithms Artificial Intelligence Artificial neural networks Belt conveyors Classification Complex Systems Computer Science Evolutionary algorithms Evolutionary Biology Fault diagnosis Machine learning Neural networks Nodes Optimization Optimization algorithms Parameters Processor Architectures Theory of Computation Brain storm optimization algorithm Variable-length Class-specific cost regulation extreme learning machine The class imbalance problems Adaptive
ISSN:	1567-7818, 1572-9796
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Abstract	Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
AbstractList	Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
Author	Guo, Yi-nan Zhang, Pei Chen, Jingjing Cheng, Jian Cheng, Shi Yang, Linkai
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Keywords	Brain storm optimization algorithm Variable-length Class-specific cost regulation extreme learning machine The class imbalance problems Adaptive
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Snippet	Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters,...
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SubjectTerms	Adaptive algorithms Artificial Intelligence Artificial neural networks Belt conveyors Classification Complex Systems Computer Science Evolutionary algorithms Evolutionary Biology Fault diagnosis Machine learning Neural networks Nodes Optimization Optimization algorithms Parameters Processor Architectures Theory of Computation
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Title	Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning
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