Hybrid Structure-Adaptive RBF-ELM Network Classifier

Wen, Hui; Fan, Hongguang; Xie, Weixin; Pei, Jihong

doi:10.1109/access.2017.2740420

Cited by 23 publications

(8 citation statements)

References 29 publications

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“…Except for the DM, CC, and IS datasets, all benchmark datasets are imbalanced datasets. In each dataset, the inputs to all the classifiers are scaled to appropriately [−1, 1]; the classification performance of each network is measured by the overall (η o ) and average (η a ) per-category classification accuracies [23]. Table 1 gives the description of the classification datasets.…”

Section: Resultsmentioning

confidence: 99%

“…To generate the optimal number and parameters of the RBF kernel, in our previous work, an incremental learning algorithm for the hybrid RBF-BP network (ILRBF-BP) [22] and a hybrid structure adaptive RBF-ELM network (HSARBF-ELM) [23] are presented. In ILRBF-BP, the method of potential density clustering is presented to generate RBF kernels automatically, which utilizes the global distribution information of sample space.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Wen

Chen

et al. 2021

Mathematical Problems in Engineering

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An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Wen

Chen

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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“…where the H + denotes the pseudoinverse of matrix H. Therefore, ELM has the advantages of fast learning and well generalization performance and has been extensively applied in many fields [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Huang et al first proposed the primary concept and theory of ELM in [17].…”

Section: Introductionmentioning

confidence: 99%

“…In [24], Huang et al applied ELM to the regression and multiclass classification. Wen et al proposed a hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) network in [25]. Based on acoustic feature transfer learning, Deng et al used ELM for recognizing emotions from whispered speech [26].…”

Section: Introductionmentioning

confidence: 99%

Toward deep neural networks: Mirror extreme learning machines for pattern classification

Liao

et al. 2020

Filomat

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In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.

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“…The key feature of ELM is that the input-hidden-layer parameters can be given at random and need not to be tuned, and this feature greatly reduces the training computational cost and mostly retains strong generalization ability [8], [22]- [24]. Meanwhile, ELM with the output weights solved by regularized least squares has been proved to maintain the unique optimal fixed point [25], [26], and the stability proof about randomly setting of hidden layer parameters have been given in [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Online ADMM-Based Extreme Learning Machine for Sparse Supervised Learning

Song

Liu

et al. 2019

IEEE Access

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Sparse learning is an efficient technique for feature selection and avoiding overfitting in machine learning research areas. Considering sparse learning for real-world problems with online learning demands in neural networks, an online sparse supervised learning of extreme learning machine (ELM) algorithm is proposed based on alternative direction method of multipliers (ADMM), termed OAL1-ELM. In OAL1-ELM, an 1-regularization penalty is added in loss function for generating a sparse solution to enhance the generalization ability. This convex combinatorial loss function is solved by using ADMM in a distributed way. Furthermore, an improved ADMM is used to reduce computational complexity and to achieve online learning. The proposed algorithm can learn data one-by-one or batch-by-batch. The convergence analysis for the fixed point of the solution is given to show the efficiency and optimality of the proposed method. The experimental results show that the proposed method can obtain a sparse solution and have strong generalization performance in a wide range of regression tasks, multiclass classification tasks, and a real-world industrial project. INDEX TERMS Online learning, alternative direction method of multipliers (ADMM), 1-regularization, extreme learning machine (ELM), sparse output parameters.

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Hybrid Structure-Adaptive RBF-ELM Network Classifier

Cited by 23 publications

References 29 publications

An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Toward deep neural networks: Mirror extreme learning machines for pattern classification

Online ADMM-Based Extreme Learning Machine for Sparse Supervised Learning

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