The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Redundancy based feature selection for microarray data
Abstract
In gene expression microarray data analysis, selecting a small number of discriminative genes from thousands of genes is an important problem for accurate classification of diseases or phenotypes. The problem becomes particularly challenging due to the large number of features (genes) and small sample size. Traditional gene selection methods often select the top-ranked genes according to their individual discriminative power without handling the high degree of redundancy among the genes. Latest research shows that removing redundant genes among selected ones can achieve a better representation of the characteristics of the targeted phenotypes and lead to improved classification accuracy. Hence, we study in this paper the relationship between feature relevance and redundancy and propose an efficient method that can effectively remove redundant genes. The efficiency and effectiveness of our method in comparison with representative methods has been demonstrated through an empirical study using public microarray data sets.
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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 1×
“A subset of features is selected from top of the ranking list [ 18 ].”
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Cited by
Toward integrating feature selection algorithms for classification and clustering
Liu, H., & Yu, L. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.66 · Cited by 2,706
Self-citationA review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
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
MIFS-ND: A mutual information-based feature selection method
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.019 · Cited by 442
Metadata
- Authors
- Yu, L., & Liu, H.
- Venue
- Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2004-08-22
- Pages
- 737-742
- OpenAlex
- W2064901349
- Cited by
- 244
- References
- 1
- FWCI
- 42.56
- Type
- conference paper
- Language
- en
- Open access
- closed
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