Two-Stage Hybrid Extreme Learning Machine for Sequential Imbalanced Data

2016 ◽  
pp. 423-433 ◽  
Author(s):  
Wentao Mao ◽  
Jinwan Wang ◽  
Ling He ◽  
Yangyang Tian
Keyword(s):  
Extreme Learning Machine ◽  
Imbalanced Data ◽  
Two Stage ◽  
Learning Machine

ODOC-ELM: Optimal decision outputs compensation-based extreme learning machine for classifying imbalanced data

2016 ◽  
Vol 92 ◽  
pp. 55-70 ◽  
Author(s):  
Hualong Yu ◽  
Changyin Sun ◽  
Xibei Yang ◽  
Wankou Yang ◽  
Jifeng Shen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Imbalanced Data ◽  
Optimal Decision ◽  
Learning Machine

Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

2017 ◽  
Vol 232 (16) ◽  
pp. 2937-2947 ◽  
Author(s):  
Yuan Lan ◽  
Xiaohong Han ◽  
Weiwei Zong ◽  
Xiaojian Ding ◽  
Xiaoyan Xiong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Misclassification Cost ◽  
Weighted Extreme Learning Machine ◽  
Rolling Element ◽  
Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

Online Extreme Learning Machine with Hybrid Sampling Strategy for Sequential Imbalanced Data

2017 ◽  
Vol 9 (6) ◽  
pp. 780-800 ◽  
Author(s):  
Wentao Mao ◽  
Mengxue Jiang ◽  
Jinwan Wang ◽  
Yuan Li
Keyword(s):  
Extreme Learning Machine ◽  
Imbalanced Data ◽  
Sampling Strategy ◽  
Learning Machine ◽  
Hybrid Sampling

scholarly journals A multi-output two-stage locally regularized model construction method using the extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 104-112 ◽  
Author(s):  
Dajun Du ◽  
Kang Li ◽  
Xue Li ◽  
Minrui Fei ◽  
Haikuan Wang
Keyword(s):  
Extreme Learning Machine ◽  
Construction Method ◽  
Model Construction ◽  
Two Stage ◽  
Learning Machine ◽  
Regularized Model
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