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Li et al. 2020) [5]. have developed an incremental learning method based on the Multiclass SVM called ˇ±IMCSVMˇ±, which gradually improves classification ability by learning new malwares samples. Their incremental learning method is to add new classes to the existing classification model.
(Lopez et al. 2020) [6].proposed a new incremental learning approach that combines a deep functionality encoder with an open dynamic set of SVM, to solve the problem of identification people of interest (IoI) from streaming face data.
(Sanghani and Kotecha 2019) [7]. developed an incremental custom email spam filter, that is an incremental learning framework using SVM which overcomes the limitation of a static decision model to handle drift concept and deeply improves the performance of the classifier.
The proposed approaches either solve a lack of data or a problem of a large amount of data that cannot be provided to the model at once. This is a horizontal update which means an update of the data. The literature explored did not address vertical updates that involve features. But the UFILA approach, our proposal, makes a vertical update.
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IEEE/ICACT20220029 Slide.04
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