Abstract
Machine learning methods have been applied to infer activities of users. However, the small number of training samples and their primitive representation often complicates the learning task. In order to correctly infer inhabitant’s behavior a long time of observation and data collection is needed. This article suggests the use of MFE3/GA\(^{D\!R}\), an evolutionary constructive induction method. Constructive induction has been used to improve learning accuracy through transforming the primitive representation of data into a new one where regularities are more apparent. The use of MFE3/GA\(^{D\!R}\) is expected to improve the representation of data and behavior learning process in an intelligent environment. The results of the research show that by applying MFE3/GA\(^{D\!R}\) a standard learner needs considerably less data to correctly infer user’s behavior.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Philipose, M., Fishkin, K., Perkowitz, M., Patterson, D., Fox, D., Kautz, H., Hähnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Computing, 50–57 (2004)
Campo, E., Chan, M.: Detecting abnormal behavior by real-time monitoring of patients. In: AAAI Workshop on Automation as Caregiver, pp. 8–12. AAAI Press (2002)
Tapia, E., Intille, S., Larson, K.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
Mozer, M.: An intelligent environment must be adaptive. Intelligent Systems and Their Applications 14, 11–13 (1999)
Das, S., Cook, D., Battacharya, A., Heierman, L.T.Y.: The role of prediction algorithms in the mavhome smart home architecture. IEEE in Wireless Communications 9, 77–84 (2002)
Aggrawal, J., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding 73(3), 428–440 (1999)
Bourke, A., Lyons, G.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering and Physics 30(1), 84–90 (2008)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.: Activity recognition from accelerometer data. American Association for Artificial Intelligence 3, 1541–1546 (2005)
Huang, P.C., Lee, S.S., Kuo, Y.H., Lee, K.R.: A flexible sequence alignment approach on pattern mining and matching for human activity recognition. Expert Systems with Applications 37, 298–306 (2010)
Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection: A Data Mining Perspective. The International Series in Engineering and Computer Science, SECS, vol. 453. Kluwer Academic Publishers, Norwell (1998)
Shafti, L.S.: Multi-feature Construction based on Genetic Algorithms and Non-algebraic Feature Representation to Facilitate Learning Concepts with Complex Interactions. PhD thesis, Escuela Politecnica Superior, Universidad Autonoma de Madrid (2008)
Shafti, L.S., Pérez, E.: Evolutionary multi-feature construction for data reduction: A case study. Journal of Applied Soft Computing 9(4), 1296–1303 (2009a)
Shafti, L.S., Pérez, E.: Feature Construction and Feature Selection in Presence of Attribute Interactions. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 589–596. Springer, Heidelberg (2009)
Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.: A Long-Term Evaluation of Sensing Modalities for Activity Recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483–500. Springer, Heidelberg (2007)
Guan, D., Yuan, W., Lee, Y., Gavrilov, A., Lee, S.: Activity recognition based on semi-supervised learning. In: The 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 469–475. IEEE Computer Society (2007)
Lustrek, M., Kaluza, B.: Fall detection and activity recognition with machine learning. Informatica 33, 205–212 (2009)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: An activity monitoring system for elderly care using generative and discriminative models. Personal and Ubiquitous Computing 14, 489–498 (2010)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Review 16(3), 177–199 (2001)
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Liu, H., Motoda, H. (eds.): Computational Methods of Feature Selection. Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC, New York (2007)
Zhao, Z., Liu, H.: Searching for interacting features in subset selection. Intelligent Data Analysis 13, 207–228 (2009)
Estébanez, C., Valls, J.M., Aler, R.: GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains. Applied Intelligence 29(2), 174–185 (2008)
Zhang, Y., Rockett, P.I.: Domain-independent feature extraction for multi-classification using multi-objective genetic programming. Pattern Analysis and Applications 13 (2009)
Zupan, B., Bratko, I., Bohanec, M., Demsar, J.: Function Decomposition in Machine Learning. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 71–101. Springer, Heidelberg (2001)
Alfred, R.: DARA: Data summarisation with feature construction. In: Asia International Conference on Modelling and Simulation, pp. 830–835. IEEE Computer Society, Kuala Lumpur (2008)
Grunwald, P.D.: The Minimum Description Length Principle. MIT Press (2007)
Boulle, M.: Khiops: A statistical discretization method of continuous attributes. Machine Learning 55, 53–69 (2004)
Kurgan, L.A., Cios, K.J.: CAIM discretization algorithm. IEEE Transactions on Knowledge and Data Engineering 16(2), 145–153 (2004)
García-Herranz, M., Haya, P.A., Montoro, G., Esquivel, A., Alamán, X.: Easing the smart home: Semi-automatic adaptation in perceptive environments. Journal of Universal Computer Science 14, 1529–1544 (2008)
Esquivel, A., Haya, P.A., García-Herranz, M., Alamán, X.: Managing Pervasive Environment Privacy Using the “fair trade” Metaphor. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 804–813. Springer, Heidelberg (2007)
Haya, P.A., Montoro, G., Alamán, X.: A Prototype of a Context-Based Architecture for Intelligent Home Environments. In: Meersman, R., Tari, Z. (eds.) CoopIS/DOA/ODBASE 2004. LNCS, vol. 3290, pp. 477–491. Springer, Heidelberg (2004)
Pidwirny, M.: Daily and annual cycles of temperature, fundamentals of physical geography, 2nd edn. (2006), http://www.physicalgeography.net/fundamentals/7l.html
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
BoxLab Wiki Page, http://boxlab.wikispaces.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shafti, L.S., Haya, P.A., García-Herranz, M., Pérez, E. (2012). Evolutionary Feature Extraction to Infer Behavioral Patterns in Ambient Intelligence. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds) Ambient Intelligence. AmI 2012. Lecture Notes in Computer Science, vol 7683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34898-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-34898-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34897-6
Online ISBN: 978-3-642-34898-3
eBook Packages: Computer ScienceComputer Science (R0)