Iterative subset selection for feature drifting data streams

Author:

Yuan Lanqin1,Pfahringer Bernhard2,Barddal Jean Paul3

Affiliation:

1. University of Waikato, Hamilton, New Zealand

2. University of Auckland, Auckland, New Zealand

3. Pontifícia Universidade Católica do Paraná, Curitiba, Brazil

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Adaptive Streaming Feature Selection Technique for Classifying Non-Stationary Data Streams;2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT);2023-10-26

2. Incremental permutation feature importance (iPFI): towards online explanations on data streams;Machine Learning;2023-09-20

3. Productive teaming under uncertainty: when a human and a machine classify objects together;2023 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO);2023-06-05

4. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams;Machine Learning and Knowledge Discovery in Databases: Research Track;2023

5. Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features;Discovery Science;2023

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