메뉴 건너뛰기




Volumn 24, Issue 7, 2010, Pages 680-710

A random decision tree ensemble for mining concept drifts from noisy data streams

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA HANDLING; DATA MINING; DECISION TREES; LEARNING ALGORITHMS;

EID: 77955356202     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839514.2010.499500     Document Type: Article
Times cited : (19)

References (46)
  • 3
    • 68349150835 scopus 로고    scopus 로고
    • On classification and segmentation of massive audio data streams
    • Aggarwal, C. C. 2009. On classification and segmentation of massive audio data streams. Knowledge and Information Systems 20:137-156.
    • (2009) Knowledge and Information Systems , vol.20 , pp. 137-156
    • Aggarwal, C.C.1
  • 4
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 7
    • 48649098791 scopus 로고    scopus 로고
    • Online mining of frequent sets in data streams with error guarantee
    • Dang, X. H., W. K. Ng, and K. L. Ong. 2008. Online mining of frequent sets in data streams with error guarantee. Knowledge and Information Systems 16(2):245-258.
    • (2008) Knowledge and Information Systems , vol.16 , Issue.2 , pp. 245-258
    • Dang, X.H.1    Ng, W.K.2    Ong, K.L.3
  • 8
    • 58549093382 scopus 로고    scopus 로고
    • Pattern-based time-series subsequence clustering using radial distribution functions
    • Denton, A. M., C. A. Besemann, and D. H. Dorr. 2009. Pattern-based time-series subsequence clustering using radial distribution functions. Knowledge and Information Systems 18(1):1-27.
    • (2009) Knowledge and Information Systems , vol.18 , Issue.1 , pp. 1-27
    • Denton, A.M.1    Besemann, C.A.2    Dorr, D.H.3
  • 12
    • 65549139674 scopus 로고    scopus 로고
    • Data stream mining using granularity-based approach
    • Gaber, M. M. 2009. Data stream mining using granularity-based approach. Foundations of Computational Intelligence 6:47-66.
    • (2009) Foundations of Computational Intelligence , vol.6 , pp. 47-66
    • Gaber, M.M.1
  • 13
    • 25444479065 scopus 로고    scopus 로고
    • Learning decision trees from dynamic data streams
    • Gama, J. 2005. Learning decision trees from dynamic data streams. Journal of Universal Computer Science 11(8):1353-1366.
    • (2005) Journal of Universal Computer Science , vol.11 , Issue.8 , pp. 1353-1366
    • Gama, J.1
  • 20
    • 84947403595 scopus 로고
    • Probability inequalities for sums of bounded random variables
    • Hoeffding, W. 1963. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58(301):13-30.
    • (1963) Journal of the American Statistical Association , vol.58 , Issue.301 , pp. 13-30
    • Hoeffding, W.1
  • 22
    • 34548766695 scopus 로고    scopus 로고
    • A semi-random multiple decision-tree algorithm for mining data streams
    • Hu, X. G., P. P. Li, X. D. Wu, and G. Q. Wu. 2007. A semi-random multiple decision-tree algorithm for mining data streams. Journal of Computer Science and Technology 22(5):711-724.
    • (2007) Journal of Computer Science and Technology , vol.22 , Issue.5 , pp. 711-724
    • Hu, X.G.1    Li, P.P.2    Wu, X.D.3    Wu, G.Q.4
  • 25
    • 37749050180 scopus 로고    scopus 로고
    • Dynamic weighted majority: An ensemble method for drifting concepts
    • Kolter, J. Z., and M. A. Maloof. 2007. Dynamic weighted majority: an ensemble method for drifting concepts. Journal of Machine Learning Research 8:2755-2790.
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 2755-2790
    • Kolter, J.Z.1    Maloof, M.A.2
  • 27
    • 85181554037 scopus 로고    scopus 로고
    • Random ensemble decision trees for learning concept-drifting data streams
    • Submitted for publication to
    • Li, P. P., Q. H. Liang, X. D. Wu, and X. G. Hu. 2010. Random ensemble decision trees for learning concept-drifting data streams. Submitted for publication to New Generation Computing.
    • (2010) New Generation Computing
    • Li, P.P.1    Liang, Q.H.2    Wu, X.D.3    Hu, X.G.4
  • 29
    • 48849089269 scopus 로고    scopus 로고
    • Adaptive machine learning in delayed feedback domains by selective relearning
    • Ludl, M. C., A. Lewandowski, and G. Dorffner. 2008. Adaptive machine learning in delayed feedback domains by selective relearning. Applied Artificial Intelligence 22(6):543-557.
    • (2008) Applied Artificial Intelligence , vol.22 , Issue.6 , pp. 543-557
    • Ludl, M.C.1    Lewandowski, A.2    Dorffner, G.3
  • 32
    • 67651208402 scopus 로고    scopus 로고
    • Hybrid grey relational artificial neural network and auto regessive integrated moving average model for forecasting time-series Data
    • Sallehuddin, R., and S. M. Hj. Shamsuddin. 2009. Hybrid grey relational artificial neural network and auto regessive integrated moving average model for forecasting time-series Data. Applied Artificial Intelligence 23(5):443-486.
    • (2009) Applied Artificial Intelligence , vol.23 , Issue.5 , pp. 443-486
    • Sallehuddin, R.1    Shamsuddin, S.M.Hj.2
  • 33
    • 0010012318 scopus 로고
    • Incremental learning from noisy data
    • Schlimmer, J. C., and R. H. Granger, Jr. 1986. Incremental learning from noisy data. Machine Learning 1(3):317-354.
    • (1986) Machine Learning , vol.1 , Issue.3 , pp. 317-354
    • Schlimmer, J.C.1    Granger Jr., R.H.2
  • 37
    • 22544451786 scopus 로고    scopus 로고
    • Learning concept drift with a committee of decision trees
    • University of Texas at Austin
    • Stanley, K. O. 2003. Learning concept drift with a committee of decision trees. Technical Report AI-03-302, University of Texas at Austin.
    • (2003) Technical Report AI-03-302
    • Stanley, K.O.1
  • 43
    • 33749587572 scopus 로고    scopus 로고
    • An automatic construction and organization strategy for ensemble learning on data streams
    • Zhang, Y., and X. M. Jin. 2006. An automatic construction and organization strategy for ensemble learning on data streams. ACM SIGMOD Record 35(3):28-33.
    • (2006) ACM SIGMOD Record , vol.35 , Issue.3 , pp. 28-33
    • Zhang, Y.1    Jin, X.M.2
  • 45
    • 43249088014 scopus 로고    scopus 로고
    • Tracking clusters in evolving data streams over sliding windows
    • Zhou, A., F. Cao, W. N. Qian, and C. Q. Jin. 2008. Tracking clusters in evolving data streams over sliding windows. Knowledge and Information Systems 15(2):181-214.
    • (2008) Knowledge and Information Systems , vol.15 , Issue.2 , pp. 181-214
    • Zhou, A.1    Cao, F.2    Qian, W.N.3    Jin, C.Q.4
  • 46
    • 33645543384 scopus 로고    scopus 로고
    • Effective classification of noisy Data Streams with attributeoriented dynamic classifier selection
    • Zhu, X. Q., X. D. Wu, and Y. Yang. 2006. Effective classification of noisy Data Streams with attributeoriented dynamic classifier selection. Knowledge and Information Systems 9(3):339-363.
    • (2006) Knowledge and Information Systems , vol.9 , Issue.3 , pp. 339-363
    • Zhu, X.Q.1    Wu, X.D.2    Yang, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.