Online Feature Selection is a dynamic feature selection paradigm that incrementally evaluates and incorporates relevant features in real-time as data samples arrive sequentially, distinguishing itself from batch or offline approaches by its adaptive, sample-by-sample processing nature.
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237
Helmut Gräbner, Horst Bischof · 2006 · 984 citations
Online Feature Selection with Streaming Features
Xindong Wu, Kui Yu, Wei Ding et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2012 · 282 citations
1–5 of 237
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Hefei University of Technology
Hefei, China
18 h-index
45 publications
4.3K citations
Minnan Normal University
Zhangzhou, China
10 h-index
20 publications
820 citations
University of Massachusetts Boston
Boston, United States
10 h-index
13 publications
966 citations
University of Vermont
Burlington, United States
10 h-index
11 publications
800 citations
University of Louisiana at Lafayette
Lafayette, United States
10 h-index
14 publications
895 citations
Yanshan University
Qinhuangdao, China
7 h-index
36 publications
1K citations
Nanyang Technological University
Singapore, Singapore
5 h-index
11 publications
1.5K citations
Xi'an Jiaotong University
Xi'an, China
4 h-index
4 publications
83 citations
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9 publications · 258 citations
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- IEEE Access
6 publications · 312 citations
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- ACM Transactions on Knowledge Discovery from Data
TKDD · 6 publications · 324 citations
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