Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications

By Yijun Sun. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

355 citations recorded by the source corpus.

Subfields: Education

View Paper: https://www.semanticscholar.org/paper/26472ac2ffa98374721e4ca227fc19580e9ebd23

How researchers see this paper

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In Education

1. Jason H. Moore
2. Kuanquan Wang
3. Wangmeng Zuo
4. Ryan J. Urbanowicz
5. Wei Yang
6. William La Cava
7. Randal S. Olson
8. Melissa Meeker

Citations in the corpus

• Relief-Based Feature Selection: Introduction and Review (2017, methodology): “…methods are available that can be applied before, after, or integrated with RBA feature selection to remove feature redundancies (Bins and Draper, 2001; Fl´orez-L´opez, 2002; Yang and Li, 2006; Sun, 2007; Challita et al., 2015; Agre and Dzhondzhorov, 2016; Liu et al., 2015; Guyon et al., 2003).”
• Neighborhood Component Feature Selection for High-Dimensional Data (2012, methodology): “As in [10], [13], each feature variable in the raw data is normalized to[0, 1].”
• Large-margin nearest neighbor classifiers via sample weight learning (2011, neutral): “All rights reserved.”
• Stable Feature Selection for Biomarker Discovery (2010, neutral): “The computational issue of combinatorial search for feature subset can to some extent be alleviated by using a feature weighting strategy [80].”