Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
Hybrid sampling extreme learning machine for sequential imbalanced data
MAO Wentao, WANG Jinwan, HE Ling, YUAN Peiyan
Journal of Computer Applications    2015, 35 (8): 2221-2226.   DOI: 10.11772/j.issn.1001-9081.2015.08.2221
Abstract546)      PDF (882KB)(468)       Save

Many traditional machine learning methods tend to get biased classifier which leads to lower classification precision for minor class in sequential imbalanced data. To improve the classification accuracy of minor class, a new hybrid sampling online extreme learning machine on sequential imbalanced data was proposed. This algorithm could improve the classification accuracy of minor class as well as reduce the loss of classification accuracy of major class, which contained two stages. In offline stage, the principal curve was introduced to model the confidence regions of minor class and major class respectively based on the strategy of balanced samples. Over-sampling of minority and under-sampling of majority was achieved based on confidence region. Then the initial model was established. In online stage, only the most valuable samples of major class were chosen according to the sample importance, and then the network weight was updated dynamically. The proposed algorithm had upper bound of the information loss through the theoretical proof. The experiment was taken on two UCI datasets and the real-world air pollutant forecasting dataset of Macao. The experimental results show that, compared with the existing methods such as Online Sequential Extreme Learning Machine (OS-ELM), Extreme Learning Machine (ELM) and Meta-Cognitive Online Sequential Extreme Learning Machine (MCOS-ELM), the proposed method has higher prediction precision and better numerical stability.

Reference | Related Articles | Metrics
Weighted online sequential extreme learning machine based on imbalanced sample-reconstruction
WANG Jinwan, MAO Wentao, HE Ling, WANG Liyun
Journal of Computer Applications    2015, 35 (6): 1605-1610.   DOI: 10.11772/j.issn.1001-9081.2015.06.1605
Abstract698)      PDF (842KB)(630)       Save

Many traditional machine learning methods tend to get biased classifier which leads to low classification precision for minor class in imbalanced online sequential data. To improve the classification accuracy of minor class, a new weighted online sequential extreme learning machine based on imbalanced sample-reconstruction was proposed. The algorithm started from exploiting distributed characteristics of online sequential data, and contained two stages. In offline stage, the principal curve was introduced to construct the confidence region, where over-sampling was achieved for minor class to construct the equilibrium sample set which was consistent with the sample distribution trend, and then the initial model was established. In online stage, a new weighted method was proposed to update sample weight dynamically, where the value of weight was related to training error. The proposed method was evaluated on UCI dataset and Macao meteorological data. Compared with the existing methods, such as Online Sequential-Extreme Learning Machine (OS-ELM), Extreme Learning Machine (ELM)and Meta-Cognitive Online Sequential- Extreme Learning Machine (MCOS-ELM), the experimental results show that the proposed method can identify the minor class with a higher ability. Moreover, the training time of the proposed method has not much difference compared with the others, which shows that the proposed method can greatly increase the minor prediction accuracy without affecting the complexity of algorithm.

Reference | Related Articles | Metrics