Feature selection in machine learning: A new perspective

By Jie Cai, Jiawei Luo, Shulin Wang, Sheng Yang. Neurocomputing, 2018

1,812 citations recorded by the source corpus.

Abstract High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.

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

View Paper: https://www.semanticscholar.org/paper/f6d9106b0e169204a506eb1deec2b85e0f296e4a

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

The relationship between trust in AI and trustworthy machine learning technologies (2019, neutral): “cluding end-users) in a humane manner [6, 17, 19, 33, 41, 42, 60, 62, 69, 76, 86, 89].”
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning (2020, background): “This can be viewed as a feature selection process (Alvarez et al., 2012; Cai et al., 2018; Oliveira et al., 2003; Opitz, 1999; Rokach, 2010), and we have confirmed it for NTK in practice, see Figure 1.”
A comprehensive survey on recent metaheuristics for feature selection (2022, neutral): “discussed evaluation measures for feature selection [78].”