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That incremental feature adding just made it fragile".
The incremental feature extraction methods are the best choice to handle such issues [74].
The DMA system learns continuously through incremental feature adaptation and weight transfer.
Then the features are carefully selected by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) methods.
Personally, I think it's a shame that so many great AI minds are working on comparatively incremental feature enhancements.
Zhou et al. [49] demonstrate that the incremental feature learning method quickly converges to the optimal number of features in a large-scale online setting.
This kind of incremental feature extraction is useful in applications where the distribution of data changes with respect to time in massive online data streams.
Zhou et al. [49] describe how a Deep Learning algorithm can be used for incremental feature learning on very large datasets, employing denoising autoencoders [50].
The Maximum Relevance Minimum Redundancy (mRMR) method combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection.
Two novel incremental feature weighting strategies are proposed in this paper, one based on a leave-one-feature-out, the other based on a feature-wise separability criterion.
The basic idea of our approach is that, instead of permanently changing the list of most important features with newly loaded data blocks, we generalize the concept of incremental feature selection to an incremental feature weighting approach: features are assigned weights in [0,1] according to their importance level.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com