Top-k Feature Selection Framework Using Robust 0-1 Integer Programming

Feature selection (FS), which identifies the relevant features in a data set to facilitate subsequent data analysis, is a fundamental problem in machine learning and has been widely studied in recent years. Most FS methods rank the features in order of their scores based on a specific criterion and...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 7; s. 3005 - 3019
Hlavní autori:	Zhang, Xiaoqin, Fan, Mingyu, Wang, Di, Zhou, Peng, Tao, Dacheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	0-1 integer programming Computer science Constraints Correlation Data analysis Datasets Equivalence Fans Feature extraction Feature selection feature selection (FS) Integer programming Learning algorithms Linear programming Machine learning nonconvex optimization norm Optimization Robustness
ISSN:	2162-237X, 2162-2388, 2162-2388
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