The DOI matches its Crossref record.
ON AUTOMATIC FEATURE SELECTION
Abstract
We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms. We compare these methods to facilitate the planning of future research on feature selection.
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Cited by
EMG feature evaluation for movement control of upper extremity prostheses
Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M. (1995) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.481972 · Cited by 477
Metadata
- Authors
- SIEDLECKI, W., & SKLANSKY, J.
- Venue
- International Journal of Pattern Recognition and Artificial Intelligence
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 1988-06
- Volume
- 02
- Issue
- 02
- Pages
- 197-220
- ISSN
- 0218-0014 (ISSN-L)
- 1793-6381
- OpenAlex
- W2130759652
- Cited by
- 392
- References
- 0
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: ON AUTOMATIC FEATURE SELECTION. https://app.cochat.ai/papers/10.1142/s0218001488000145
- The paper
- SIEDLECKI, W., & SKLANSKY, J. (1988). ON AUTOMATIC FEATURE SELECTION. International Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/s0218001488000145
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
