Abstract
Adaptation in context-aware ubiquitous environments and adaptive systems is becoming more and more complex. Adaptations need to take into account information from a plethora of heterogeneous sensors, while the adaptation decisions often imply personalised aspects and individual preferences, which are likely to change over time. We present a novel concept for lifelong learning from sensor data streams for predictive user modelling that is applicable in scenarios where simpler mechanisms that rely on pre-trained general models fall short. With the LiLoLe-Framework, we pursue an approach that allows ubiquitous systems to continuously learn from their users and adapt the system at the same time through stream-based active learning. This Framework can guide the development of context-aware or adaptive systems in form of an overall architecture.
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Aggarwal, C.: An Introduction to Data Streams. In: Aggarwal, C. (ed.) Data Streams - Models and Algorithms, pp. 1–8. Springer, Heidelberg (2007)
Aggarwal, C.: Data Streams: An Overview and Scientific Applications. In: Gaber, M.M. (ed.) Scientific Data Mining and Knowledge Discovery - Principles and Foundations, pp. 377–397. Springer, Heidelberg (2010)
Atlas, L.E., Cohn, D.A., Ladner, R.E.: Training Connectionist Networks with Queries and Selective Sampling. In: Neural Information Processing Systems, Denver, CO, USA, November 27-30, pp. 566–573. Morgan Kaufmann Publishers Inc., San Fransisco (1989)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: Proc. of the Twenty-First ACM Symposium on Principles of Database Systems - PODS 2002, Madison, WI, USA, pp. 1–16. ACM Press, New York (2002)
Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Bifet, A.: Adaptive Stream Mining - Pattern Learning and Mining from Evolving Data Streams. IOS Press, Amsterdam (2010)
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-training. In: Proc. of the 11th Annual Conference on Computational Learning Theory, COLT 1998, Madison, WI, USA, pp. 92–100. ACM Press, New York (1998)
Bryll, R., Gutierrez-Osuna, R., Quek, F.: Attribute Bagging: Improving Accuracy of Classifier Ensembles by Using Random Feature Subsets. Pattern Recognition 36(6), 1291–1302 (2003)
Consolvo, S., Smith, I.E., Matthews, T., LaMarca, A., Tabert, J., Powledge, P.: Location Disclosure to Social Relations: Why, When, & What People Want to Share. In: Proc. of the Conference on Human Factors in Computing Systems - CHI 2005, Portland, USA, April 2-7, pp. 81–90. ACM Press, New York (2005)
Fetter, M., Seifert, J., Gross, T.: Predicting Selective Availability for Instant Messaging. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part III. LNCS, vol. 6948, pp. 503–520. Springer, Heidelberg (2011)
Fogarty, J., Hudson, S.E., Akteson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J.: Predicting Human Interruptibility with Sensors. ACM Transactions on Computer-Human Interaction (TOCHI) 12(1), 119–146 (2005)
Gama, J.: Issues and Challenges in Learning from Data Streams. In: Kargupta, H., Han, J., Yu, P.S., Motwani, R., Kumar, V. (eds.) Next Generation of Data Mining. Chapman & Hall/CRC, Tailor & Francis Group, Boca Raton (2008)
Gama, J., Rodriques, P.P.: Data Stream Processing. In: Gama, J., Gaber, M.M. (eds.) Learning from Data Streams - Processing Techniques in Sensor Networks, pp. 25–39. Springer, Heidelberg (2007)
Gross, T., Egla, T., Marquardt, N.: Sensation: A Service-Oriented Platform for Developing Sensor-Based Infrastructures. IJIPT 1(3), 159–167 (2006)
Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE TPAMI 20(8), 832–844 (1998)
Horvitz, E., Kapoor, A.: Experience Sampling for Building Predictive User Models: A Comparative Study. In: Proc. of the Conference on Human Factors in Computing Systems, CHI 2008, Florence, Italy, pp. 657–666. ACM Press, New York (2008)
Horvitz, E., Koch, P., Apacible, J.: BusyBody: Creating and Fielding Personalized Models of the Cost of Interruption. In: Proc. of the 2004 ACM Conference on Computer Supported Cooperative Work, CSCW 2004, Chicago, Illinois, November 6-10, pp. 507–510. ACM Press, New York (2004)
Kapoor, A., Horvitz, E.: Principles of Lifelong Learning for Predictive User Modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 37–46. Springer, Heidelberg (2007)
Keogh, E., Selina, C., Hart, D., Pazzani, M.: Segmenting Time Series: A Survey and Novel Approach. In: Last, M., Kandel, A., Bunke, H. (eds.) Data Mining in Time Series Databases, pp. 1–22. World Scientific Publishing Co., Singapore (2003)
Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection - A Data Mining Perspective. Kluwer Academic Publishers, Norwell (1998)
Markovitch, S., Rosenstein, D.: Feature Generation Using General Constructor Functions. Machine Learning 49(1), 59–98 (2002)
Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Reiss, A., Stricker, D.: Personalized Mobile Physical Activity Recognition. In: Proc. of the 2013 International Symposium on Wearable Computers - ISWC 2013, Zurich, Switzerland, September 9-12, pp. 25–28. ACM Press, New York (2013)
Rokach, L.: Ensemble-based Classifiers. AI Review 33(1-2), 1–39 (2010)
Salber, D., Dey, A.K., Abowd, G.D.: The Context Toolkit: Aiding the Development of Context-Enabled Applications. In: Proc. of the Conference on Human Factors in Computing Systems- CHI 1999, Pittsburgh, PA, USA, May 15-20, pp. 434–441. ACM Press, New York (1999)
Schirmer, M., Gross, T.: CollaborationBus Aqua: Easy Cooperative Editing of Ubiquitous Environments. In: Proc. of the International Conference on Collaborative Technologies - CT 2010, Freiburg, Germany, July 26-28, pp. 77–84. IADIS Press (2010)
Settles, B.: Active Learning. Morgan & Claypool Publishers, San Rafael (2012)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining Concept-Drifting Data Streams Using Ensemble Classifiers. In: Proc. of the Ninth ACM International Conference on Knowledge Discovery and Data Mining - KDD 2003, Washington, D.C., USA, August 24-27, pp. 226–235. ACM Press, New York (2003)
Wang, J., Zhao, P., Hoi, S.C.H., Jin, R.: Online Feature Selection and Its Applications. IEEE Transactions on Knowledge and Data Engineering (2013)
Webb, G.I., Pazzani, M.J., Billsus, D.: Machine Learning for User Modeling. User Modeling and User-Adapted Interaction (UMUAI) 11(1-2), 19–29 (2001)
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23(1), 69–101 (1996)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2011)
Wu, X., Yu, K., Ding, W., Wang, H., Zhu, X.: Online Feature Selection with Streaming Features. IEEE TPAMI 35(5), 1178–1192 (2013)
Wu, X., Yu, K., Wang, H., Ding, W.: Online Streaming Feature Selection. In: Procedings of the 27th International Conference on Machine Learning, ICML 2010, Haifa, Israel, June 21-24, pp. 1159–1166. Omnipress, Madison (2010)
Zhao, Z., Chen, Y., Liu, J., Shen, Z., Liu, M.: Cross-People Mobile-Phone Based Activity Recognition. In: Proc. of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, Barcelona, Catalonia, Spain, pp. 2545–2550. AAAI Press, Menlo Park (2011)
Žliobaitė, I.e., Bifet, A., Pfahringer, B., Holmes, G.: Active Learning with Evolving Streaming Data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 597–612. Springer, Heidelberg (2011)
Zukerman, I., Albrecht, D.W.: Predictive Statistical Models for User Modeling. User Modeling and User-Adapted Interaction (UMUAI) 11(1-2), 5–18 (2001)
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Fetter, M., Gross, T. (2014). LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling. In: Sauer, S., Bogdan, C., Forbrig, P., Bernhaupt, R., Winckler, M. (eds) Human-Centered Software Engineering. HCSE 2014. Lecture Notes in Computer Science, vol 8742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44811-3_8
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DOI: https://doi.org/10.1007/978-3-662-44811-3_8
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