Context-aware collaborative data stream mining in ubiquitous devices

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

Volumn 7014 LNCS, Issue , 2011, Pages 22-33

  Bártolo Gomes, João ^a   Gaber, Mohamed Medhat ^b   Sousa, Pedro A C ^c   Menasalvas, Ernestina ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF PORTSMOUTH   (United Kingdom)

^c UNIVERSIDADE NOVA DE LISBOA   (Portugal)

Author keywords

Collaborative Data Stream Mining; Concept Drift; Context awareness; Ubiquitous Knowledge Discovery

Indexed keywords

CLASSIFICATION ACCURACY; CONCEPT DRIFT; CONTEXT INFORMATION; CONTEXT-AWARE; CONTEXT-AWARENESS; DATA STREAM; DATA STREAM MINING; ENSEMBLE METHODS; INTELLIGENT DECISION MAKING; SYSTEM STATE; UBIQUITOUS APPLICATION; UBIQUITOUS DEVICES;

DATA REDUCTION; DECISION MAKING;

DATA COMMUNICATION SYSTEMS;

EID: 80455127184     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24800-9_5     Document Type: Conference Paper

References (27)

1. Brezillon, R., Pomerol, J.C.: Contextual knowledge sharing and cooperation in intelligent assistant systems. Travail Humain 62, 223-246 (1999)
2. Cortez, P., Lopes, C., Sousa, P., Rocha, M., Rio, M.: Symbiotic Data Mining for Personalized Spam Filtering. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT 2009, vol. 1, pp. 149-156. IEEE, Los Alamitos (2009)
3. Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed data mining in peer-to-peer networks. IEEE Internet Computing, 18-26 (2006)
4. Daumé III, H., Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26(1), 101-126 (2006)
5. Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16(2), 97-166 (2001)
6. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71-80. ACM, New York (2000)
7. Gaber, M.M., Krishnaswamy, S., Zaslavsky, A.: Ubiquitous data stream mining. In: Current Research and Future Directions Workshop Proceedings held in conjunction with The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia. Citeseer (2004)
8. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286-295. Springer, Heidelberg (2004)
9. Harries, M.B., Sammut, C., Horn, K.: Extracting hidden context. Machine Learning 32(2), 101-126 (1998)
10. Hotho, A., Pedersen, R.,Wurst, M.: Ubiquitous Data. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery. LNCS, vol. 6202, pp. 61-74. Springer, Heidelberg (2010)
11. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97-106. ACM, New York (2001)
12. Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., et al.: VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. In: Proceedings of SIAM International Conference on Data Mining, vol. 334 (2004)
13. Katakis, I., Tsoumakas, G., Vlahavas, I.: On the utility of incremental feature selection for the classification of textual data streams. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 338-348. Springer, Heidelberg (2005)
14. Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research 8, 2755-2790 (2007)
15. Padovitz, A., Loke, S.W., Zaslavsky, A.: Towards a theory of context spaces. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004, pp. 38-42 (2004)
16. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 1345-1359 (2009)
17. Schmidt, A., Beigl, M., Gellersen, H.W.: There is more to context than location. Computers & Graphics 23(6), 893-901 (1999)
18. Stahl, F., Gaber, M.M., Bramer, M., Yu, P.S.: Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 2, pp. 323-330. IEEE, Los Alamitos (2010)
19. Street, W.N., Kim, Y.S.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377-382. ACM, New York (2001)
20. Tsymbal, A.: The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin (2004)
21. Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Inf. Fusion 9, 56-68 (2008)
22. Turney, P.D.: Exploiting context when learning to classify. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 402-407. Springer, Heidelberg (1993)
23. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 226-235. ACM, New York (2003)
24. Widmer, G.: Tracking context changes through meta-learning. Machine Learning 27(3), 259-286 (1997)
25. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine learning 23(1), 69-101 (1996)
26. Wurst, M., Morik, K.: Distributed feature extraction in a p2p setting-a case study. Future Generation Computer Systems 23(1), 69-75 (2007)
27. Zhu, X., Wu, X., Yang, Y.: Effective classification of noisy data streams with attribute-oriented dynamic classifier selection. Knowl. Inf. Syst. 9, 339-363 (2006)