Open challenges for data stream mining research

310 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning (2022, background): “Due to storage constraints [26, 14] or privacy issues [10, 6], a practical Class-Incremental Learning (CIL) [40] model requires the ability to update with incoming instances from new classes without revisiting former data.”
Class-Incremental Learning: A Survey (2023, methodology): “These streaming data cannot be held for long due to storage constraints [5] or privacy issues [6], requiring the model to be updated incrementally with only new class instances.”
Deep Class-Incremental Learning: A Survey (2023, methodology): “These streaming data cannot be held for long due to storage constraints [8], [9] or privacy issues [10], [11], requiring the model to be updated incrementally with only new class instances.”
Characterizing concept drift (2015, background): “This paper does not directly address issues of how to detect and handle concept drift from data, for which interested readers are encouraged to read the numerous existing excellent tutorials and surveys (Bifet et al. 2011; Nguyen et al. 2014; Gama and Rodrigues 2009; Aggarwal 2009; Gaber et al. 2005”