OLR-WA Online Regression with Weighted Average
This is an incremental improvement for online learning in machine learning, addressing storage and recalculation issues with streaming data.
The paper tackles the problem of online linear regression by introducing OLR-WA, a method that combines new data with an existing model using weighted averages, allowing users to adjust bias toward old or new data. Results show it performs similarly to batch models for consistent data and offers flexibility for varying data.
Machine Learning requires a large amount of training data in order to build accurate models. Sometimes the data arrives over time, requiring significant storage space and recalculating the model to account for the new data. On-line learning addresses these issues by incrementally modifying the model as data is encountered, and then discarding the data. In this study we introduce a new online linear regression approach. Our approach combines newly arriving data with a previously existing model to create a new model. The introduced model, named OLR-WA (OnLine Regression with Weighted Average) uses user-defined weights to provide flexibility in the face of changing data to bias the results in favor of old or new data. We have conducted 2-D and 3-D experiments comparing OLR-WA to a static model using the entire data set. The results show that for consistent data, OLR-WA and the static batch model perform similarly and for varying data, the user can set the OLR-WA to adapt more quickly or to resist change.