I am working with attribute incremental learning ( or virtual concept drift) which is every time when a new feature will be available for a real-time dataset (i.e. any online auction dataset) a classifier will add that new feature with the existing features in a dataset and classify the new dataset (with previous features and new features) incrementally. I know that we can convert a stationary classifier to an incremental classifier in python. However, I could not find any detail information on how to add the new features in an incremental classifier continuously in real-time. It would be great if anyone could give me some insight on this .
https://stackoverflow.com/questions/59740034/attribute-incremntal-learning-virtual-concept-drift
https://ieeexplore.ieee.org/abstract/document/6248112/
Article Incremental Learning with Respect to New Incoming Input Attributes
I hope you may get some insight from Article Streaming chunk incremental learning for class-wise data str...
and https://arxiv.org/pdf/1910.01568.pdf
This is a good paper:
http://vipl.ict.ac.cn/uploadfile/upload/2019040110344534.pdf
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