scholarly journals Dual-Weighted Kernel Extreme Learning Machine for Hyperspectral Imagery Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 508
Author(s):  
Xumin Yu ◽  
Yan Feng ◽  
Yanlong Gao ◽  
Yingbiao Jia ◽  
Shaohui Mei
Keyword(s):  
Extreme Learning Machine ◽  
Dimensional Space ◽  
Hyperspectral Imagery ◽  
Data Sets ◽  
Spatial Features ◽  
Weighted Extreme Learning Machine ◽  
Public Data ◽  
Weighted Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Due to its excellent performance in high-dimensional space, the kernel extreme learning machine has been widely used in pattern recognition and machine learning fields. In this paper, we propose a dual-weighted kernel extreme learning machine for hyperspectral imagery classification. First, diverse spatial features are extracted by guided filtering. Then, the spatial features and spectral features are composited by a weighted kernel summation form. Finally, the weighted extreme learning machine is employed for the hyperspectral imagery classification task. This dual-weighted framework guarantees that the subtle spatial features are extracted, while the importance of minority samples is emphasized. Experiments carried on three public data sets demonstrate that the proposed dual-weighted kernel extreme learning machine (DW-KELM) performs better than other kernel methods, in terms of accuracy of classification, and can achieve satisfactory results.

Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

2017 ◽  
Vol 26 (1) ◽  
pp. 185-195 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Xin Zhao
Keyword(s):  
Extreme Learning Machine ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Data Sets ◽  
Rbf Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

scholarly journals Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 2036 ◽  
Author(s):  
Jiaojiao Li ◽  
Bobo Xi ◽  
Qian Du ◽  
Rui Song ◽  
Yunsong Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Low Cost ◽  
Principal Component ◽  
Learning Machines ◽  
Spatial Features ◽  
Classification Framework ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Deep Feature Extraction

Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new classification framework derived from the deep KELM network and GFFPC is presented to generate deep spectral and spatial features. Experimental results demonstrate that the proposed framework outperforms some state-of-the-art algorithms with very low cost, which can be used for real-time processes.

A Feature Weighted Kernel Extreme Learning Machine Ensemble Method for Gas Turbine Fault Diagnosis

2020 ◽  
Author(s):  
Liping Yan ◽  
Xuezhi Dong ◽  
Hualiang Zhang ◽  
Haisheng Chen
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Information Gain ◽  
Feature Space ◽  
Ensemble Method ◽  
Separating Hyperplane ◽  
Weighted Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Abstract Fault diagnosis is a very important section of gas turbine maintenance. Kernel extreme learning machine (KELM), a novel artificial intelligence algorithm, is a potentially effective diagnosis technology. The existing KELMs are all assumed that there is the same influence to the optimal separating hyperplane from all features, which reduces its generalization performance. In this study, a feature weighted kernel extreme learning machine ensemble method (FWKELM-RF) is developed for application in the field of gas turbine fault diagnosis. First, information gain ratio is introduced to assign different weights to the feature space. Furthermore, random forest is used to enhance stable performance of feature weighted KELM. The fault datasets from a gas turbine with three shafts is generated to validate the performance of the developed method, and the results demonstrate that FWKELM-RF can achieve better accuracy and stability for detecting fault in gas turbine.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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