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Imbalanced Data Classification for Multi-source Heterogenous Sensor Networks
2020
IEEE Access
Most of the traditional classification algorithms are based on the uniform distribution of samples, and the effect is not ideal when dealing with such data, which mainly shows that the classification results incline to the majority class. Therefore, we propose the imbalanced multi-source heterogeneous data classification algorithms in this paper, which are mainly based on the expansion and extension of Support Vector Machines. Considering that there are complex connections within multi-source
doi:10.1109/access.2020.2966324
fatcat:h3eta76sh5hdvphgtz76lfmwiy