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A Direct Method of Nonparametric Measurement Selection
Abstract
A direct method of measurement selection is proposed to determine the best subset of d measurements out of a set of D total measurements. The measurement subset evaluation procedure directly employs a nonparametric estimate of the probability of error given a finite design sample set. A suboptimum measurement subset search procedure is employed to reduce the number of subsets to be evaluated. Teh primary advantage of the approach is the direct but nonparametric evaluation of measurement subsets, for the M class problem.
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Citations
Cites
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
Cited by
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Particle swarm optimization for feature selection in classification: a multi-objective approach
Xue, B., Zhang, M., & Browne, W. N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2227469 · Cited by 1,282
A review of feature selection methods based on mutual information
Vergara, J. R., & Estévez, P. A. (2014) · Neural Computing and Applications · doi:10.1007/s00521-013-1368-0 · Cited by 1,255
Zhang, Y., Gong, D.-W., & Cheng, J. (2017) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2476796 · Cited by 379
Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., & van Ginneken, B. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2011480 · Cited by 361
Metadata
- Authors
- Whitney, A. W.
- Venue
- IEEE Transactions on Computers
- Published
- 1971-09
- Volume
- C-20
- Issue
- 9
- Pages
- 1100-1103
- ISSN
- 0018-9340 (ISSN-L)
- 1557-9956
- 2326-3814
- OpenAlex
- W2072026441
- Cited by
- 851
- References
- 1
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- closed
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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.