Paper Info
Title
An ELM-based model with sparse-weighting strategy for sequential data imbalance problem.
Abstract
In many practical engineering applications, online sequential data imbalance problems are universally found. Many traditional machine learning methods are hard to improve the classification accuracy effectively while solving these problems. To get fast and efficient classification, a new online sequential extreme learning machine algorithm with sparse-weighting strategy is proposed to increase the accuracy of minority class while reducing the accuracy loss of majority class as much as possible. The main idea is integrating a new sparse-weighting strategy into the present data-based strategy for sequential data imbalance problem. In offline stage, a two phase balanced strategies is introduced to obtain the valuable virtual sample set. In online stage, a dynamic weighting strategy is proposed to assign the corresponding weight for each sequential sample by means of the change of sensitivity and specificity in order to maintain the optimal network structure. Experimental results on two kinds of imbalanced datasets, UCI datasets and the real-world air pollutant forecasting dataset, show that the proposed method has higher prediction accuracy and better numerical stability compared with ELM, OS-ELM, meta-cognitive OS-ELM and weighted OS-ELM.
Year
DOI
Venue
2017
10.1007/s13042-016-0509-z
Int. J. Machine Learning & Cybernetics
Keywords
Field
DocType
Extreme learning machine, Imbalance problem, Online sequential learning
Data mining,Online machine learning,Sequential data,Weighting,Computer science,Extreme learning machine,Online sequential,Artificial intelligence,Data imbalance,Machine learning,Numerical stability,Network structure
Journal
Volume
Issue
ISSN
8
4
1868-808X
Citations 
PageRank 
References 
27
0.69
19
Authors
3
Name
Order
Citations
PageRank
Wentao Mao111211.54
Jinwan Wang2371.83
Zhanao Xue3270.69