Imbalanced Data Classification for Multi-source Heterogenous Sensor Networks

Wei Wang, Mengjun Zhang, Li Zhang, Qiong Bai
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
more » ... ta, express them as a unified, concise and efficient mathematical model can completely retain data information and improve data processing efficiency. We perform tensor representation and feature extraction on the heterogeneous data, and two different classification algorithms are proposed in this paper. In the first method, we represent multisource heterogeneous data into a unified tensor form directly and obtain a high-quality core data through dimensionality reduction algorithm, then realize data classification by Support Tensor Machine. In the other method, we extract data from different data sources and classify them with Ensemble Deep Support Vector Machine (DSVM), which combined three DSVM with different kernel functions. The algorithms are compared on CUAVE data set, which contains two different modalities of sound and picture. INDEX TERMS Heterogeneous sensor network, imbalanced data, ensemble deep support vector machine, support tensors machine. 27406 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
doi:10.1109/access.2020.2966324 fatcat:h3eta76sh5hdvphgtz76lfmwiy