The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection
Abstract
Genetic algorithms have been proved to be a very efficient method in the feature selection problem. However, as for every other method, if the validation of the results is performed in an incomplete way, erroneous conclusions can be drawn. In this paper a development of a previous genetic algorithm is presented so that a full validation of the results can be obtained. Furthermore, this algorithm has been shown to perform very well also as an outlier detector, allowing easy identification of the presence of outliers even in cases where the ‘classical’ techniques fail.
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
Genetic Algorithms and Machine Learning
Goldberg, D. E., & Holland, J. H. (1988) · Machine Learning · doi:10.1023/a:1022602019183 · Cited by 3,193
Cited by
Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003) · Environmental Health Perspectives · doi:10.1289/ehp.5758 · Cited by 1,325
Genetic algorithms applied to feature selection in PLS regression: how and when to use them
Leardi, R., & Lupiáñez González, A. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00051-3 · Cited by 759
Self-citationGenetic algorithms in chemometrics and chemistry: a review
Leardi, R. (2001) · Journal of Chemometrics · doi:10.1002/cem.651 · Cited by 397
Self-citationOn-line infrared spectroscopy for bioprocess monitoring
Landgrebe, D., Haake, C., Höpfner, T., Beutel, S., Hitzmann, B., Scheper, T., Rhiel, M., & Reardon, K. F. (2010) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-010-2743-8 · Cited by 134
Metadata
- Authors
- Leardi, R.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 1994-01
- Volume
- 8
- Issue
- 1
- Pages
- 65-79
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2086936642
- Cited by
- 202
- References
- 1
- FWCI
- 12.10
- 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: Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. https://app.cochat.ai/papers/10.1002/cem.1180080107
- The paper
- Leardi, R. (1994). Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. Journal of Chemometrics. https://doi.org/10.1002/cem.1180080107
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.