The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Enhanced recursive feature elimination
Abstract
For classification with small training samples and high dimensionality, feature selection plays an important role in avoiding overfitting problems and improving classification performance. One of the commonly used feature selection methods for small samples problems is recursive feature elimination (RFE) method. RFE method utilizes the generalization capability embedded in support vector machines and is thus suitable for small samples problems. Despite its good performance, RFE tends to discard "weak" features, which may provide a significant improvement of performance when combined with other features. In this paper, we propose an enhanced recursive feature elimination (EnRFE) method for feature selection in small training sample classification. Our experimental results show that the proposed method outperforms the original RFE in terms of classification accuracy on various datasets.
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Cited by
Predictive Model to Identify College Students with High Dropout Rates
Hoyos Osorio, J. K., & Daza Santacoloma, G. (2023) · Revista Electrónica de Investigación Educativa · doi:10.24320/redie.2023.25.e13.5398 · Cited by 10
Metadata
- Authors
- Chen, X.-W., & Jeong, J. C.
- Venue
- Sixth International Conference on Machine Learning and Applications (ICMLA 2007)
- Publisher
- IEEE
- Published
- 2007-12
- Pages
- 429-435
- OpenAlex
- W1999466943
- Cited by
- 381
- References
- 20
- FWCI
- 0.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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