Online Synthesis Incremental Data Streams Classification Algorithm

Authors

Sanmin Liu, 1. College of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China;;
Yuxia Liu, 2. Center of Modern Education Technology, Anhui Polytechnic University, Wuhu 241000, China;

Abstract

Abstract: Online learning is the effective way to solve the sample's non-recurrence in data streams classification, and how to deal with the problem of sample deficiency is the critical point for improving online learning efficiency. According to the mean square error decomposition theory of the model's parameter estimation and the idea of cluster, the new samples are constructed by linear synthesis with the class center and the sample, which can improve the distribution information of sample and reduce the lower bound of parameter value. The online incremental learning is executed and the class center point is continuously updated. Through theory analysis and simulation experiment, it is suggested that the provided schema is feasible and has superiority over other algorithm.

Recommended Citation

Liu, Sanmin and Liu, Yuxia (2018) "Online Synthesis Incremental Data Streams Classification Algorithm," Journal of System Simulation: Vol. 30: Iss. 6, Article 39.
DOI: 10.16182/j.issn1004731x.joss.201806039
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss6/39

First Page

2315

Revised Date

2017-09-07

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201806039

Last Page

2320

CLC

TP393

Recommended Citation

Liu Sanmin, Liu Yuxia. Online Synthesis Incremental Data Streams Classification Algorithm[J]. Journal of System Simulation, 2018, 30(6): 2315-2320.

DOI

10.16182/j.issn1004731x.joss.201806039