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Density-based semi-supervised online sequential extreme learning machine

  • Min XiaEmail author
  • Jie Wang
  • Jia Liu
  • Liguo Weng
  • Yiqing Xu
Hybrid Artificial Intelligence and Machine Learning Technologies
  • 23 Downloads

Abstract

This paper proposes a density-based semi-supervised online sequential extreme learning machine (D-SOS-ELM). The proposed method can realize online learning of unlabeled samples chunk by chunk. Local density and distance are used to measure the similarity of patterns, and the patterns with high confidence are selected by the ‘follow’ strategy for online learning, which can improve the accuracy of learning. Through continuous patterns selection, the proposed method ultimately achieves effective learning of unlabeled patterns. Furthermore, using local density and relative distance can effectively respond to the relationship between patterns. Compared with the traditional distance-based similarity measure, the ability to deal with complex data is improved. Empirical study on several standard benchmark data sets demonstrates that the proposed D-SOS-ELM model outperforms state-of-art methods in terms of accuracy.

Keywords

Semi-supervised learning Extreme learning machine Online sequential learning Fast density clustering 

Notes

Acknowledgements

This work is supported in part by, the National Natural Science Foundation of PR China (61773219, 61503192), Natural Science Foundation of Jiangsu Province (BK20161533), and Qing Lan Project of Jiangsu Province.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Jiangsu Key Laboratory of Big Data Analysis TechnologyNanjing University of Information Science and TechnologyNanjingChina
  3. 3.College of Information Science and TechnologyNanjing Forestry UniversityNanjingChina

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