MORStreaming: A Multioutput Regression System for Streaming Data
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022, 52, (8)
- Issue Date:
- 2022-08-31
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| MORStreaming_A_Multioutput_Regression_System_for_Streaming_Data.pdf | Published version | 2.38 MB |
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With the continuous generation of huge volumes of streaming data, streaming data regression has become more complicated. A regressor that predicts two or more outputs, i.e., multioutput regression, is commonly used in many applications. However, current multioutput regressors use a batch method to handle data, which presents compatibility issues for streaming data as they need to be analyzed online. To address this issue, we present a multioutput regression system, called MORStreaming, for streaming data. MORStreaming uses an instance-based model to make predictions because this model can quickly adapt to change by only storing new instances or by throwing away old instances. However, learning instances in our regression system are constrained by online demand and need to consider the relationship between outputs. Therefore, MORStreaming consists of two algorithms: 1) an online algorithm based on topology networks which is designed to learn the instances and 2) an online algorithm based on adaptive rules which is designed to learn the correlation between outputs automatically. Experiments involving both artificial and real-world datasets indicate MORStreaming can achieve superior performance compared with other multioutput methods.
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