Selective ensemble of uncertain extreme learning machine for pattern classification with missing features

A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.

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Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-020-01235-y ◽

2021 ◽

Author(s):

Umesh Gupta ◽

Deepak Gupta

Keyword(s):

Extreme Learning Machine ◽

Pattern Classification ◽

Learning Machine

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

Filomat ◽

10.2298/fil2015985l ◽

2020 ◽

Vol 34 (15) ◽

pp. 4985-4996

Author(s):

Bolin Liao ◽

Chuan Ma ◽

Meiling Liao ◽

Shuai Li ◽

Zhiguan Huang

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Pattern Classification ◽

Simple Structure ◽

Layer Structure ◽

Feed Forward Neural Network ◽

Learning Machines ◽

S Parameter ◽

Learning Machine ◽

Hidden Layer

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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