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Volumn 70, Issue 7-9, 2007, Pages 1155-1166

Immune network based ensembles

Author keywords

Artificial immune systems; Classification; Classifier ensembles; Immune network

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; LEARNING SYSTEMS;

EID: 33847409306     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.11.005     Document Type: Article
Times cited : (20)

References (46)
  • 1
    • 33847367496 scopus 로고    scopus 로고
    • T.W. Anderson, An Introduction to Multivariate Statistical Analysis, second ed., Wiley Series in Probability and Mathematical Statistics, Wiley, New York, 1984.
  • 2
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: bagging, boosting and variants
    • Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting and variants. Mach. Learning 36 1/2 (1999) 105-142
    • (1999) Mach. Learning , vol.36 , Issue.1-2 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 3
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • Breiman L. Stacked regressions. Mach. Learning 24 1 (1996) 49-64
    • (1996) Mach. Learning , vol.24 , Issue.1 , pp. 49-64
    • Breiman, L.1
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Mach. Learning 24 2 (1996) 123-140
    • (1996) Mach. Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • Breiman L. Arcing classifiers. Ann. Stat. 26 (1998) 801-824
    • (1998) Ann. Stat. , vol.26 , pp. 801-824
    • Breiman, L.1
  • 7
    • 0033986060 scopus 로고    scopus 로고
    • The immune system as a model for pattern recognition and classification
    • Carter J.H. The immune system as a model for pattern recognition and classification. J. Am. Med. Inf. Assoc. 7 1 (2000) 28-41
    • (2000) J. Am. Med. Inf. Assoc. , vol.7 , Issue.1 , pp. 28-41
    • Carter, J.H.1
  • 10
    • 71649110512 scopus 로고    scopus 로고
    • Artificial immune systems as a novel soft computing paradigm
    • de Castro L.N., and Timmis J. Artificial immune systems as a novel soft computing paradigm. Soft Comput. J. 7 7 (2007)
    • (2007) Soft Comput. J. , vol.7 , Issue.7
    • de Castro, L.N.1    Timmis, J.2
  • 12
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learning Res. 7 (2006) 1-30
    • (2006) J. Mach. Learning Res. , vol.7 , pp. 1-30
    • Demšar, J.1
  • 13
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10 7 (1998) 1895-1923
    • (1998) Neural Comput. , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 15
    • 12144288329 scopus 로고    scopus 로고
    • Is combining classifiers with stacking better than selecting the best one?
    • Dzeroski S., and Zenko B. Is combining classifiers with stacking better than selecting the best one?. Mach. Learning 54 (2004) 255-273
    • (2004) Mach. Learning , vol.54 , pp. 255-273
    • Dzeroski, S.1    Zenko, B.2
  • 17
    • 0141921552 scopus 로고    scopus 로고
    • Online ensemble learning: an empirical study
    • Fern A., and Givan R. Online ensemble learning: an empirical study. Mach. Learning 53 (2003) 71-109
    • (2003) Mach. Learning , vol.53 , pp. 71-109
    • Fern, A.1    Givan, R.2
  • 18
    • 33847419507 scopus 로고    scopus 로고
    • Y. Freund, R. Schapire, Experiments with a new boosting algorithm, in: Proceedings of the 13 International Conference on Machine Learning, Bari, Italy, 1996, pp. 148-156.
  • 20
    • 33847370552 scopus 로고    scopus 로고
    • N. García-Pedrajas, C. Fyfe, Construction of classifier ensembles by means of artificial immune systems, J. Heuristics, in press.
  • 21
    • 33847355441 scopus 로고    scopus 로고
    • N. García-Pedrajas, C. Fyfe, Nonlinear "boosting" projections for ensemble construction, J. Mach. Learning Res., accepted.
  • 23
    • 21044454599 scopus 로고    scopus 로고
    • Cooperative coevolution of artificial neural network ensembles for pattern classification
    • García-Pedrajas N., Hervás-Martínez C., and Ortiz-Boyer D. Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans. Evol. Comput. 9 3 (2005) 271-302
    • (2005) IEEE Trans. Evol. Comput. , vol.9 , Issue.3 , pp. 271-302
    • García-Pedrajas, N.1    Hervás-Martínez, C.2    Ortiz-Boyer, D.3
  • 24
    • 21244465997 scopus 로고    scopus 로고
    • How do we evaluate artificial immune systems?
    • Garret S.M. How do we evaluate artificial immune systems?. Evol. Comput. 13 2 (2005) 145-178
    • (2005) Evol. Comput. , vol.13 , Issue.2 , pp. 145-178
    • Garret, S.M.1
  • 25
    • 78149349523 scopus 로고    scopus 로고
    • L. Hall, K. Bowyer, R. Banfield, D. Bhadoria, W. Kegelmeyer, S. Eschrich, Comparing pure parallel ensemble creation techniques against bagging, in: Third IEEE International Conference on Data Mining, Melbourne, FL, USA, 2003, pp. 533-536.
  • 26
    • 33847421626 scopus 로고    scopus 로고
    • E. Hart, P. Ross, Exploiting the analogy between immunology and spares distributed memories: a system for clustering non-stationary data, in: First International Conference on Artificial Immune Systems, Canterbury, UK, 2002, pp. 49-58.
  • 28
    • 33847415693 scopus 로고    scopus 로고
    • S. Hettich, C. Blake, C. Merz, UCI repository of machine learning databases, 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉, 1998.
  • 29
    • 0032139235 scopus 로고    scopus 로고
    • The random subspace method for constructing decision forests
    • Ho T.K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20 8 (1998) 832-844
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell. , vol.20 , Issue.8 , pp. 832-844
    • Ho, T.K.1
  • 30
    • 0015956495 scopus 로고
    • Towards a network theory of the immune system
    • Jerne K.N. Towards a network theory of the immune system. Ann. Immunol. 125C (1974) 373-389
    • (1974) Ann. Immunol. , vol.125 C , pp. 373-389
    • Jerne, K.N.1
  • 31
    • 0034186937 scopus 로고    scopus 로고
    • On the algorithmic implementation of stochastic discrimination
    • Kleinberg E. On the algorithmic implementation of stochastic discrimination. IEEE Trans. Pattern Anal. Mach. Intell. 22 5 (2000) 473-490
    • (2000) IEEE Trans. Pattern Anal. Mach. Intell. , vol.22 , Issue.5 , pp. 473-490
    • Kleinberg, E.1
  • 32
    • 0348151971 scopus 로고    scopus 로고
    • Combining classifiers: soft computing solutions
    • Pal S.K., and Pal A. (Eds), World Scientific, Singapore
    • Kuncheva L.I. Combining classifiers: soft computing solutions. In: Pal S.K., and Pal A. (Eds). Pattern Recognition: From Classical to Modern Approaches (2001), World Scientific, Singapore 427-451
    • (2001) Pattern Recognition: From Classical to Modern Approaches , pp. 427-451
    • Kuncheva, L.I.1
  • 33
    • 0034315099 scopus 로고    scopus 로고
    • Evolutionary ensembles with negative correlation learning
    • Liu Y., Yao X., and Higuchi T. Evolutionary ensembles with negative correlation learning. IEEE Trans. Evolutionary Comput. 4 4 (2000) 380-387
    • (2000) IEEE Trans. Evolutionary Comput. , vol.4 , Issue.4 , pp. 380-387
    • Liu, Y.1    Yao, X.2    Higuchi, T.3
  • 34
    • 0032661927 scopus 로고    scopus 로고
    • Using correspondence analysis to combine classifiers
    • Merz C.J. Using correspondence analysis to combine classifiers. Mach. Learning 36 1 (1999) 33-58
    • (1999) Mach. Learning , vol.36 , Issue.1 , pp. 33-58
    • Merz, C.J.1
  • 35
    • 10444270597 scopus 로고    scopus 로고
    • Forming neural networks through efficient and adaptive coevolution
    • Moriarty D.E., and Miikkulainen R. Forming neural networks through efficient and adaptive coevolution. Evol. Comput. 4 5 (1997) 373-399
    • (1997) Evol. Comput. , vol.4 , Issue.5 , pp. 373-399
    • Moriarty, D.E.1    Miikkulainen, R.2
  • 37
  • 38
    • 0024472594 scopus 로고
    • Immune network theory
    • Perelson A. Immune network theory. Immunol. Rev. 110 (1989) 5-36
    • (1989) Immunol. Rev. , vol.110 , pp. 5-36
    • Perelson, A.1
  • 39
    • 0000926506 scopus 로고
    • When networks disagree: ensemble methods for hybrid neural networks
    • Mammone R.J. (Ed), Chapman & Hall, London
    • Perrone M.P., and Cooper L.N. When networks disagree: ensemble methods for hybrid neural networks. In: Mammone R.J. (Ed). Neural Networks for Speech and Image Processing (1993), Chapman & Hall, London 126-142
    • (1993) Neural Networks for Speech and Image Processing , pp. 126-142
    • Perrone, M.P.1    Cooper, L.N.2
  • 40
    • 84957007471 scopus 로고    scopus 로고
    • M. Skurichina, R.P.W. Duin, Bagging and the random subspace method for redundant feature spaces, in: J. Kittler, R. Poli (Eds.), Proceedings of the Second International Workshop on Multiple Classifier Systems MCS 2001, Cambridge, UK, 2001, pp. 1-10.
  • 42
    • 0034247206 scopus 로고    scopus 로고
    • Multiboosting: a technique for combining boosting and wagging
    • Webb G.I. Multiboosting: a technique for combining boosting and wagging. Mach. Learning 40 2 (2000) 159-196
    • (2000) Mach. Learning , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 43
    • 0001884644 scopus 로고
    • Individual comparisons by ranking methods
    • Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1 (1945) 80-83
    • (1945) Biometrics , vol.1 , pp. 80-83
    • Wilcoxon, F.1
  • 44
    • 0031143030 scopus 로고    scopus 로고
    • A new evolutionary system for evolving artificial neural networks
    • Yao X., and Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8 3 (1997) 694-713
    • (1997) IEEE Trans. Neural Networks , vol.8 , Issue.3 , pp. 694-713
    • Yao, X.1    Liu, Y.2
  • 45
    • 24644517147 scopus 로고    scopus 로고
    • Selective svms ensemble driven by immune clonal algorithm
    • Rothlauf F. (Ed), Springer, Berlin
    • Zhang X., Wang S., Shan T., and Jiao L. Selective svms ensemble driven by immune clonal algorithm. In: Rothlauf F. (Ed). Proceedings of EvoWorkshops (2005), Springer, Berlin 325-333
    • (2005) Proceedings of EvoWorkshops , pp. 325-333
    • Zhang, X.1    Wang, S.2    Shan, T.3    Jiao, L.4
  • 46
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: many could be better than all
    • Zhou Z.-H., Wu J., and Tang W. Ensembling neural networks: many could be better than all. Artif. Intell. 137 1-2 (2002) 239-253
    • (2002) Artif. Intell. , vol.137 , Issue.1-2 , pp. 239-253
    • Zhou, Z.-H.1    Wu, J.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.