Hybrid neural network extreme learning machine and flower pollination algorithm to predict fire extensions on Kalimantan Island

2021 ◽  
Author(s):  
N. Nalaratih ◽  
A. Damayanti ◽  
E. Winarko
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Flower Pollination Algorithm ◽  
Hybrid Neural Network ◽  
Flower Pollination ◽  
Learning Machine

scholarly journals Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1583
Author(s):  
Ting Liu ◽  
Qinwei Fan ◽  
Qian Kang ◽  
Lei Niu
Keyword(s):  
Extreme Learning Machine ◽  
Credit Rating ◽  
Nonlinear Function ◽  
Flower Pollination Algorithm ◽  
Algorithm Optimization ◽  
Classification Problems ◽  
Generalization Performance ◽  
Flower Pollination ◽  
Fast Training ◽  
Learning Machine

Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

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