Exploring concept drift using interactive simulations

2013 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2013

Volumn , Issue , 2013, Pages 49-54

  Smith, Jeremiah ^a   Dulay, Naranker ^a   Toth, Mate Attila ^b   Amft, Oliver ^b   Zhang, Yanxia ^c  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^c LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Activity Recognition; Adaptive Learners; Concept Drift; Machine Learning; Semi supervised Learning; Unsupervised Learning

Indexed keywords

ACCURATE PREDICTION; ACTIVITY RECOGNITION; ADAPTIVE LEARNERS; CONCEPT DRIFTS; INTERACTIVE SIMULATIONS; OPTIMAL SOLUTIONS; SEMI-SUPERVISED LEARNING; UNSUPERVISED ADAPTATION;

LEARNING SYSTEMS; PATTERN RECOGNITION; SUPERVISED LEARNING; UNSUPERVISED LEARNING;

UBIQUITOUS COMPUTING;

EID: 84881500997     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PerComW.2013.6529455     Document Type: Conference Paper

References (15)

1. O. Baños, M. Damas, H. Pomares, I. Rojas, M. T́oth, and O. Amft. A benchmark dataset to evaluate sensor displacement in activity recognition. In UbiComp '12, 2012.
2. J. Bayati, H.and del R. Milĺan and R. Chavarriaga. Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition. In ISWC '11, 2011.
3. K. Förster, P. Brem, D. Roggen, and G. Tröster. Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks. In ISSNIP 2009, 2009.
4. K. Forster, D. Roggen, and G. Troster. Unsupervised classifier self-calibration through repeated context occurences: Is there robustness against sensor displacement to gain? In ISWC '09, 2009.
5. I. Katakis, G. Tsoumakas, E. Banos, N. Bassiliades, and I. Vlahavas. An adaptive personalized news dissemination system. J. Intell. Inf. Syst., 2009.
6. K. Kunze and P. Lukowicz. Dealing with sensor displacement in motion-based onbody activity recognition systems. In UbiComp '08, 2008.
7. P. Lindstrom, S. J. Delany, and B. MacNamee. Autopilot: Simulating changing concepts in real data. In D. Bridge, K. Brown, B O'Sullivan, and H Sorensen, editors, AICS-08, 2008.
8. B. Logan, J. Healey, M. Philipose, E. Tapia, and S. Intille. A long-term evaluation of sensing modalities for activity recognition. In UbiComp '07, 2007.
9. L. Minku, A. White, and X. Yao. The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. on Knowl. and Data Eng., 2010.
10. A. Narasimhamurthy and L. Kuncheva. A framework for generating data to simulate changing environments. In AIAP'07, 2007.
11. M. Stikic, D. Larlus, S. Ebert, and B. Schiele. Weakly supervised recognition of daily life activities with wearable sensors. IEEE Trans. Pattern Anal. Mach. Intell., 2011.
12. M. Stikic, K. Van Laerhoven, and B. Schiele. Exploring semi-supervised and active learning for activity recognition. In ISWC '11, 2008.
13. A. Tsymbal, M. Pechenizkiy, P. Cunningham, and S. Puuronen. Handling local concept drift with dynamic integration of classifiers: Domain of antibiotic resistance in nosocomial infections. In CBMS, 2006.
14. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 1996.
15. I. Zliobaite. Learning under concept drift: an overview. CoRR, abs/1010.4784, 2010.