The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
Open accessAbstract
This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA.
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Citations
Cites
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 5×
“Therefore, it makes good sense that features with high score in ReliefF are more likely to help 1NN identify the correct nearest neighbor, hence generating good classification accuracy.”
InfluentialKhan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644
Cited by
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
Neighborhood rough set based heterogeneous feature subset selection
Hu, Q., Yu, D., Liu, J., & Wu, C. (2008) · Information Sciences · doi:10.1016/j.ins.2008.05.024 · Cited by 1,085
Xue, B., Zhang, M., & Browne, W. N. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2013.09.018 · Cited by 614
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
Self-citationBinary differential evolution with self-learning for multi-objective feature selection
Zhang, Y., Gong, D.-W., Gao, X.-Z., Tian, T., & Sun, X.-Y. (2020) · Information Sciences · doi:10.1016/j.ins.2019.08.040 · Cited by 429
Metadata
- Authors
- Zhu, Z., Ong, Y.-S., & Dash, M.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2007-02
- Volume
- 37
- Issue
- 1
- Pages
- 70-76
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2165885026
- Cited by
- 443
- References
- 3
- FWCI
- 28.37
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhu, Z., Ong, Y.-S., & Dash, M. (2007). Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/tsmcb.2006.883267
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