The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
A Review of Feature Selection and Its Methods
Open accessAbstract
Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.
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Cited by
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3089435 · Cited by 360
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
Elmannai, H., El-Rashidy, N., Mashal, I., Alohali, M. A., Farag, S., El-Sappagh, S., & Saleh, H. (2023) · Diagnostics · doi:10.3390/diagnostics13081506 · Cited by 140
Broer, A. A. R., Benedictus, R., & Zarouchas, D. (2022) · Aerospace · doi:10.3390/aerospace9040183 · Cited by 86
A Feature Selection Method for Multimodal Multispectral LiDAR Sensing
Han, Y., Salido-Monzú, D., Butt, J. A., Schweizer, S., & Wieser, A. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.04.022 · Cited by 6
Metadata
- Authors
- Venkatesh, B., & Anuradha, J.
- Venue
- Cybernetics and Information Technologies
- Publisher
- Walter de Gruyter GmbH
- Published
- 2019-03-01
- Volume
- 19
- Issue
- 1
- Pages
- 3-26
- ISSN
- 1311-9702 (ISSN-L)
- 1314-4081
- OpenAlex
- W2925507233
- Cited by
- 631
- References
- 92
- FWCI
- 22.68
- Type
- journal article
- Language
- en
- Open access
- diamond
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