The DOI matches its Crossref record; 156 of the 156 references it cites were found in a registry or an index.
Feature selection in machine learning: A new perspective
Open accessAbstract
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.
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Cites
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Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
Bommert, A., Welchowski, T., Schmid, M., & Rahnenführer, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab354 · Cited by 182
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Allen-Zhu, Z., & Li, Y. (2020) · arXiv · doi:10.48550/arxiv.2012.09816 · Cited by 178
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Metadata
- Authors
- Cai, J., Luo, J., Wang, S., & Yang, S.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 300
- Pages
- 70-79
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2791315675
- Cited by
- 2,061
- References
- 156
- FWCI
- 71.03
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
