The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Feature subset selection using a genetic algorithm
Abstract
Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
An Introduction to Genetic Algorithms
Mitchell, M. (1996) · doi:10.7551/mitpress/3927.001.0001 · Cited by 11,340
Cited by
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189
Markov blanket-embedded genetic algorithm for gene selection
Zhu, Z., Ong, Y.-S., & Dash, M. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.02.007 · Cited by 494
Rolling element bearing fault diagnosis using convolutional neural network and vibration image
Hoang, D.-T., & Kang, H.-J. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.03.002 · Cited by 450
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
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
Metadata
- Authors
- Yang, J., & Honavar, V.
- Venue
- IEEE Intelligent Systems
- Published
- 1998-03
- Volume
- 13
- Issue
- 2
- Pages
- 44-49
- ISSN
- 1094-7167 (ISSN-L)
- OpenAlex
- W2087327261
- Cited by
- 1,362
- References
- 2
- FWCI
- 24.94
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Feature subset selection using a genetic algorithm. https://app.cochat.ai/papers/10.1109/5254.671091
- The paper
- Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. IEEE Intelligent Systems. https://doi.org/10.1109/5254.671091
Data sources
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.
