메뉴 건너뛰기




Volumn 4585 LNAI, Issue , 2007, Pages 574-583

Combining answers of sub-classifiers in the bagging-feature ensembles

Author keywords

Aggregation rules; Bagging; Feature selection; Machine learning; Multiple classifiers

Indexed keywords

ASSOCIATION RULES; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; ROUGH SET THEORY; SAMPLING;

EID: 38049016947     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73451-2_60     Document Type: Conference Paper
Times cited : (6)

References (20)
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L.: Bagging predictors. Machine Learning 24(2), 123-140 (1996)
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 4
    • 80053403826 scopus 로고    scopus 로고
    • Dietrich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, 1857, pp. 1-15. Springer, Heidelberg (2000)
    • Dietrich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
  • 5
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proc. 17th Conf. on Machine Learning (2000)
    • (2000) Proc. 17th Conf. on Machine Learning
    • Hall, M.1
  • 7
    • 0031224390 scopus 로고    scopus 로고
    • Use of contextual information for feature ranking and discretization
    • Hong, S.J.: Use of contextual information for feature ranking and discretization. IEEE Transactions on Knowledge and Data. Engineering 9, 718-730 (1997)
    • (1997) IEEE Transactions on Knowledge and Data. Engineering , vol.9 , pp. 718-730
    • Hong, S.J.1
  • 8
    • 85021112585 scopus 로고
    • Feature subset selection using the wrapper method: Overfitting and dynamic search space topology
    • Montreal, pp, AAAI Press, Stanford, California
    • Kohavi, R., Sommerfield, D.: Feature subset selection using the wrapper method: overfitting and dynamic search space topology. In: Proceedings of the 1st Int. Conference on Knowledge Discovery and Data Mining, Montreal, pp. 192-197. AAAI Press, Stanford, California (1995)
    • (1995) Proceedings of the 1st Int. Conference on Knowledge Discovery and Data Mining , pp. 192-197
    • Kohavi, R.1    Sommerfield, D.2
  • 10
    • 33645971775 scopus 로고    scopus 로고
    • Mixing bagging and multiple feature subsets to improve classification accuracy of decision tree combination
    • Tilburg University
    • Latinne, P., Debeir, O., Decaestecker, Ch.: Mixing bagging and multiple feature subsets to improve classification accuracy of decision tree combination. In: Proc. of the 10th Belgian-Dutch Conf. on Machine Learning, Tilburg University (2000)
    • (2000) Proc. of the 10th Belgian-Dutch Conf. on Machine Learning
    • Latinne, P.1    Debeir, O.2    Decaestecker, C.3
  • 13
    • 84948984870 scopus 로고    scopus 로고
    • Ensemble feature selection based on contextual merit and correlation heuristics
    • Caplinskas, A, Eder, J, eds, ADBIS 2001, Springer, Heidelberg
    • Puuronen, S., Skrypnyk, I., Tsymbal, A.: Ensemble feature selection based on contextual merit and correlation heuristics. In: Caplinskas, A., Eder, J. (eds.) ADBIS 2001. LNCS, vol. 2151, pp. 155-168. Springer, Heidelberg (2001)
    • (2001) LNCS , vol.2151 , pp. 155-168
    • Puuronen, S.1    Skrypnyk, I.2    Tsymbal, A.3
  • 14
    • 2942715016 scopus 로고    scopus 로고
    • An experimental evaluation of improving rule based classifiers with two approaches that change representations of learning examples
    • Stefanowski, J.: An experimental evaluation of improving rule based classifiers with two approaches that change representations of learning examples. Engineering Applications of Artificial Intelligence Journal 17, 439-445 (2004)
    • (2004) Engineering Applications of Artificial Intelligence Journal , vol.17 , pp. 439-445
    • Stefanowski, J.1
  • 15
    • 9444277234 scopus 로고    scopus 로고
    • The bagging and n2-classifiers based on rules induced by MODLEM
    • Tsumoto, S, Słowiński, R, Komorowski, J, Grzymala-Busse, J.W, eds, RSCTC 2004, Springer, Heidelberg
    • Stefanowski, J.: The bagging and n2-classifiers based on rules induced by MODLEM. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymala-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 488-497. Springer, Heidelberg (2004)
    • (2004) LNCS (LNAI , vol.3066 , pp. 488-497
    • Stefanowski, J.1
  • 17
    • 84974706809 scopus 로고    scopus 로고
    • Tsymbal, A., Puuronen, S.: Bagging and Boosting with dynamic integration of classifiers. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), 1910, pp. 116-125. Springer, Heidelberg (2000)
    • Tsymbal, A., Puuronen, S.: Bagging and Boosting with dynamic integration of classifiers. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 116-125. Springer, Heidelberg (2000)
  • 19
    • 84865801454 scopus 로고    scopus 로고
    • Ensambles of learning machines
    • Marinaro, M, Tagliaferri, R, eds, Neural Nets, Springer, Heidelberg
    • Valentini, G., Masuli, F.: Ensambles of learning machines. In: Marinaro, M., Tagliaferri, R. (eds.) Neural Nets. LNCS, vol. 2486, pp. 3-19. Springer, Heidelberg (2002)
    • (2002) LNCS , vol.2486 , pp. 3-19
    • Valentini, G.1    Masuli, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.