Centralized vs. distributed feature selection methods based on data complexity measures

By L. Morán-Fernández, Verónica Bolón-Canedo, Amparo Alonso-Betanzos. 2017

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

How researchers see this paper

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

In Artificial Intelligence

1. Yang Liu
2. Qiang Yang
3. Tianjian Chen
4. Yan Kang
5. Yong Cheng
6. Xinwei Zhang
7. Mingyi Hong
8. Liping Li

In Computer Vision and Pattern Recognition

1. Yang Liu
2. Yong Cheng
3. Tianjian Chen
4. Yan Kang
5. Qiang Yang
6. Mingyi Hong
7. Victor O. K. Li
8. Felix Stahlberg

In Molecular Biology

1. Qiang Yang
2. Mingyi Hong
3. Yang Liu

Citations in the corpus

• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022, background): “Note although not considered in our experiments, in real-world settings, some features may be overlapping or distributedfeatureselection[38],[39]mayneedtobeperformed.”