메뉴 건너뛰기




Volumn 91, Issue , 2016, Pages 919-926

A Survey on Feature Selection

Author keywords

clustering; feature selection; machine learning; unsupervised

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; COMPUTER VISION; DATA MINING; LEARNING SYSTEMS;

EID: 84984993984     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2016.07.111     Document Type: Conference Paper
Times cited : (436)

References (40)
  • 1
    • 27844550205 scopus 로고    scopus 로고
    • Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach
    • L. Wolf, and A. Shashua Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach The Journal of Machine Learning Research 6 2005 1855 1887
    • (2005) The Journal of Machine Learning Research , vol.6 , pp. 1855-1887
    • Wolf, L.1    Shashua, A.2
  • 5
    • 70449102559 scopus 로고    scopus 로고
    • Semi-supervised feature selection via spectral analysis
    • Z. Zhao, H. Liu, Semi-supervised feature selection via spectral analysis., in: SDM, SIAM, 2007, pp. 641-646.
    • (2007) SDM, SIAM , pp. 641-646
    • Zhao, Z.1    Liu, H.2
  • 6
    • 77954565155 scopus 로고    scopus 로고
    • Discriminative semi-supervised feature selection via manifold regularization, Neural Networks
    • Z. Xu, I. King, M.R.-T. Lyu, and R. Jin Discriminative semi-supervised feature selection via manifold regularization, Neural Networks IEEE Transactions on 21 7 2010 1033 1047
    • (2010) IEEE Transactions on , vol.21 , Issue.7 , pp. 1033-1047
    • Xu, Z.1    King, I.2    Lyu, M.R.-T.3    Jin, R.4
  • 13
    • 4344667429 scopus 로고    scopus 로고
    • A Bayesian approach to joint feature selection and classifier design, Pattern Analysis and Machine Intelligence
    • B. Krishnapuram, A. Harternink, L. Carin, and M.A. Figueiredo A bayesian approach to joint feature selection and classifier design, Pattern Analysis and Machine Intelligence IEEE Transactions on 26 9 2004 1105 1111
    • (2004) IEEE Transactions on , vol.26 , Issue.9 , pp. 1105-1111
    • Krishnapuram, B.1    Harternink, A.2    Carin, L.3    Figueiredo, M.A.4
  • 14
    • 79955444979 scopus 로고    scopus 로고
    • The fisher-markov selector: Fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data, Pattern Analysis and Machine Intelligence
    • Q. Cheng, H. Zhou, and J. Cheng The fisher-markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data, Pattern Analysis and Machine Intelligence IEEE Transactions on 33 6 2011 1217 1233
    • (2011) IEEE Transactions on , vol.33 , Issue.6 , pp. 1217-1233
    • Cheng, Q.1    Zhou, H.2    Cheng, J.3
  • 17
  • 18
    • 17644384367 scopus 로고    scopus 로고
    • Minimum redundancy feature selection from microarray gene expression data
    • C. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data Journal of bioinformatics and computational biology 3 02 2005 185 205
    • (2005) Journal of Bioinformatics and Computational Biology , vol.3 , Issue.2 , pp. 185-205
    • Ding, C.1    Peng, H.2
  • 19
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
    • H. Peng, F. Long, and C. Ding Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy Pattern Analysis and Machine Intelligence, IEEE Transactions on 27 8 2005 1226 1238
    • (2005) Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 20
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Machine learning 46 1-3 2002 389 422
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 21
    • 0002709342 scopus 로고    scopus 로고
    • Feature selection via concave minimization and support vector machines
    • P. S. Bradley, O.L. Mangasarian, Feature selection via concave minimization and support vector machines., in: ICML, Vol. 98, 1998, pp. 82-90.
    • (1998) ICML , vol.98 , pp. 82-90
    • Bradley, P.S.1    Mangasarian, O.L.2
  • 28
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • R.A. Fisher The use of multiple measurements in taxonomic problems Annals of eugenics 7 2 1936 179 188
    • (1936) Annals of Eugenics , vol.7 , Issue.2 , pp. 179-188
    • Fisher, R.A.1
  • 30
    • 33144458972 scopus 로고    scopus 로고
    • Efficient and robust feature extraction by maximum margin criterion, Neural Networks
    • H. Li, T. Jiang, and K. Zhang Efficient and robust feature extraction by maximum margin criterion, Neural Networks IEEE Transactions on 17 1 2006 157 165
    • (2006) IEEE Transactions on , vol.17 , Issue.1 , pp. 157-165
    • Li, H.1    Jiang, T.2    Zhang, K.3
  • 31
    • 84893379490 scopus 로고    scopus 로고
    • Vector-valued multi-view semi-supervised learning for multi-label image classification, in: Proceedings of the 27th AAAI Conference on Artificial Intelligence
    • Y. Luo, D. Tao, C. Xu, and D. Li Vector-valued multi-view semi-supervised learning for multi-label image classification, in: Proceedings of the 27th AAAI Conference on Artificial Intelligence AAAI 2013 2013
    • (2013) AAAI , vol.2013
    • Luo, Y.1    Tao, D.2    Xu, C.3    Li, D.4
  • 33
    • 84908211928 scopus 로고    scopus 로고
    • A convex formulation for semi-supervised multi-label feature selection
    • X. Chang, F. Nie, Y. Yang, H. Huang, A convex formulation for semi-supervised multi-label feature selection., in: AAAI, 2014, pp. 1171-1177.
    • (2014) AAAI , pp. 1171-1177
    • Chang, X.1    Nie, F.2    Yang, Y.3    Huang, H.4
  • 34
    • 84959493567 scopus 로고    scopus 로고
    • Clustering-guided sparse structural learning for unsupervised feature selection, Knowledge and Data Engineering
    • Z. Li, J. Liu, Y. Yang, X. Zhou, and H. Lu Clustering-guided sparse structural learning for unsupervised feature selection, Knowledge and Data Engineering IEEE Transactions on 26 9 2014 2138 2150
    • (2014) IEEE Transactions on , vol.26 , Issue.9 , pp. 2138-2150
    • Li, Z.1    Liu, J.2    Yang, Y.3    Zhou, X.4    Lu, H.5
  • 36
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • U. Von Luxburg A tutorial on spectral clustering Statistics and computing 17 4 2007 395 416
    • (2007) Statistics and Computing , vol.17 , Issue.4 , pp. 395-416
    • Von Luxburg, U.1
  • 37
    • 84992726552 scopus 로고
    • Estimating attributes: Analysis and extensions of relief
    • I. Kononenko, Estimating attributes: analysis and extensions of relief, in: Machine Learning: ECML-94, Springer, 1994, pp. 171-182.
    • (1994) Machine Learning: ECML-94, Springer , pp. 171-182
    • Kononenko, I.1
  • 38
    • 84873278481 scopus 로고    scopus 로고
    • On similarity preserving feature selection, Knowledge and Data Engineering
    • Z. Zhao, L. Wang, H. Liu, and J. Ye On similarity preserving feature selection, Knowledge and Data Engineering IEEE Transactions on 25 3 2013 619 632
    • (2013) IEEE Transactions on , vol.25 , Issue.3 , pp. 619-632
    • Zhao, Z.1    Wang, L.2    Liu, H.3    Ye, J.4
  • 39
    • 84871399329 scopus 로고    scopus 로고
    • Unsupervised feature selection using nonnegative spectral analysis
    • Z. Li, Y. Yang, J. Liu, X. Zhou, H. Lu, Unsupervised feature selection using nonnegative spectral analysis., in: AAAI, 2012.
    • (2012) AAAI
    • Li, Z.1    Yang, Y.2    Liu, J.3    Zhou, X.4    Lu, H.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.