On classifying drifting concepts in P2P networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6321 LNAI, Issue PART 1, 2010, Pages 24-39

  Ang, Hock Hee ^a   Gopalkrishnan, Vivekanand ^a   Ng, Wee Keong ^a   Hoi, Steven ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

classification; Concept drift; distributed classification; peer to peer (P2P) networks

Indexed keywords

CLASSIFICATION; CLASSIFICATION PERFORMANCE; CONCEPT DRIFTS; DISTRIBUTED CLASSIFICATION; DISTRIBUTED COMPUTING ENVIRONMENT; EMPIRICAL STUDIES; LEARNING FRAMEWORKS; P2P NETWORK; PRO-ACTIVE APPROACH; REAL WORLD DATA; RESEARCH PROBLEMS;

DISTRIBUTED COMPUTER SYSTEMS; LEARNING SYSTEMS;

PEER TO PEER NETWORKS;

EID: 78049353055     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15880-3_8     Document Type: Conference Paper

References (18)

1. Ang, H. H., Gopalkrishnan, V., Ng, W. K., Hoi, S. C. H.: Communication-efficient classification in P2P networks. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML/PKDD 2009. LNCS, vol. 5782, pp. 83-98. Springer, Heidelberg (2009)
2. Bhaduri, K., Wolff, R., Giannella, C., Kargupta, H.: Distributed decision-tree induction in peer-to-peer systems. Statistical Analysis and Data Mining 1(2), 85-103 (2008)
3. Luo, P., Xiong, H., Lü, K., Shi, Z.: Distributed classification in peer-to-peer networks. In: ACM SIGKDD, pp. 968-976 (2007)
4. Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed data mining in peer-to-peer networks. Internet Computing 10(4), 18-26 (2006)
5. Chen, R., Sivakumar, K., Kargupta, H.: Distributed web mining using bayesian networks from multiple data streams. In: ICDM, pp. 75-82 (2001)
6. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD, pp. 97-106 (2001)
7. Kolter, J. Z., Maloof, M. A.: Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8, 2755-2790 (2007)
8. Street, W. N., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: KDD, pp. 377-382 (2001)
9. Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Information Fusion 9(1), 56-68 (2008)
10. Wang, H., Fan, W., Yu, P. S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD, pp. 226-235 (2003)
11. Xu, Y., Wang, K., Fu, A. W. C., She, R., Pei, J.: Classification spanning correlated data streams. In: CIKM, pp. 132-141 (2006)
12. Yang, Y., Wu, X., Zhu, X.: Mining in anticipation for concept change: Proactivereactive prediction in data streams. Data Mining and Knowledge Discovery 13(3), 261-289 (2006)
13. Hsieh, C. J., Chang, K. W., Lin, C. J., Keerthi, S. S., Sundararajan, S.: A dual coordinate descent method for large-scale linear SVM. In: ICML, pp. 408-415. ACM, New York (2008)
14. Dong, W., Wang, Z., Josephson, W., Charikar, M., Li, K.: Modeling lsh for performance tuning. In: CIKM, pp. 669-678 (2008)
15. Lemire, D.: Faster retrieval with a two-pass dynamic-time-warping lower bound. Pattern Recognition 42(9), 2169-2180 (2009)
16. Kubat, M.: A machine learning-based approach to load balancing in computer networks. Cybernetics and Systems 23 (3-4), 389-400 (1992)
17. Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P. B. (ed.) ECML 1993. LNCS, vol. 667, pp. 227-243. Springer, Heidelberg (1993)
18. Kelly, M. G., Hand, D. J., Adams, N. M.: The impact of changing populations on classifier performance. In: KDD, pp. 367-371 (1999)