Abstract
In the current paper we consider the task of object classification in wireless sensor networks. Due to restricted battery capacity, minimizing the energy consumption is a main concern in wireless sensor networks. Assuming that each feature needed for classification is acquired by a sensor, a sequential classifier combination approach is proposed that aims at minimizing the number of features used for classification while maintaining a given correct classification rate. In experiments with data from the UCI repository, the feasibility of this approach is demonstrated.
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References
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, Inc., Hoboken (2004)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Rahman, A.F.R., Fairhurst, M.C.: Serial Combination of Multiple Experts: A Unified Evaluation. Pattern Anal. Appl. 2(4), 292–311 (1999)
Zhang, Z., Zhou, S., Zhou, A.: Sequential Classifiers Combination for Text Categorization: An Experimental Study. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 509–518. Springer, Heidelberg (2004)
Chellapilla, K., Shilman, M., Simard, P.: Combining Multiple Classifiers for Faster Optical Character Recognition. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 358–367. Springer, Heidelberg (2006)
Viola, P., Jones, M.: Robust Real-Time Face Detection. Int. Journal of Computer Vision 57(2), 137–154 (2004)
Capelli, R., Maio, D., Maltoni, D., Nanni, L.: A Two-Stage Fingerprint Classification System. In: Proc. of the ACM SIGMM Workshop on Biometrics Methods and Applications, pp. 95–99. ACM, New York (2003)
Last, M., Bunke, H., Kandel, A.: A Feature-Based Serial Approach to Classifier Combination. Pattern Analysis and Applications 5, 385–398 (2002)
Xiao, Y., Chen, H., Li, F.: Handbook on Sensor Networks, World Scientific Publishing Co., Singapore (2010)
Duarte, M.F., Hu, Y.H.: Vehicle Classification in Distributed Sensor Networks. Journal of Parallel and Distributed Computing 64(7), 826–838 (2004)
Brooks, R.R., Ramanathan, P., Sayeed, A.M.: Distributed Target Classification and Tracking in Sensor Networks. Proceedings of the IEEE 91(8), 1163–1171 (2003)
Meesookho, C., Narayanan, S., Raghavendra, C.S.: Collaborative Classification Applications in Sensor Networks. In: IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, pp. 370–374 (2002)
Wang, X., Bi, D., Ding, L., Wang, S.: Agent Collaborative Target Localization and Classification in Wireless Sensor Networks. Sensors 7, 1359–1386 (2007)
Sun, Y., Qi, H.: Dynamic Target Classification in Wireless Sensor Networks. In:19th International Conference Pattern Recognition, ICPR 2008 (2008)
Gu, L., et al.: Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments. In: Proc. of the Third Int. Conference on Embedded Networked Sensor Systems, ACM, New York (2005)
Wang, L., Xiao, Y.: A Survey of Energy-Efficient Scheduling Mechanisms in Sensor Networks. Mobile Networks and Applications 11, 723–740 (2006)
Chow, C.K.: On Optimum Recognition Error and Reject Trade-Off. IEEE Transactions on Information Theory, IT 16(1), 41–46 (1970)
Fumera, G., Roli, F., Giacinto, G.: Reject Option with Multiple Thresholds. Pattern Recognition 33(12), 2099–2101 (2000)
Hanczar, B., Dougherty, E.R.: Classification with Reject Option in Gene Expression Data. Bioinformatics 24(17), 1889–1895 (2008)
Kira, K., Rendell, L.A.: The Feature Selection Problem Traditional Methods and a new Algorithm. In: Proceedings of the tenth National Conference on Artificial Intelligence, pp. 129–134. AAAI Press, Menlo Park (1992)
Pudil, P., Novovicova, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)
Csirik, J., Bunke, H.: Feature Selection and Ranking for Pattern Classification in Wireless Sensor Networks. In: Csirik, J., Bunke, H. (eds.) Pattern Recognition, Machine Intelligence and Biometrics Expanding Frontiers, Springer, Heidelberg (2011)
Zappi, P., Lombriser, C., Farelle, E., Roggen, D., Benini, L., Troester, G.: Experiences with Experiments in Ambient Intelligence Environments. In: IADIS Int. Conference Wireless Applications and Computing (2009)
Intille, S., Larson, K., Tapia, E.M., Beaudin, J.S., Kaushik, P., Nawyn, J., Rockinson, R.: Using a Live-In Laboratory for Ubiquitous Computing Research. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 349–365. Springer, Heidelberg (2006)
Frank, A., Asuncion, A.U.: Machine Learning Repository. University of California, School of Information and Computer Science, Irvine CA (2010), http://archive.ics.uci.edu/ml
Gyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)
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Csirik, J., Bertholet, P., Bunke, H. (2011). Sequential Classifier Combination for Pattern Recognition in Wireless Sensor Networks. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_21
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DOI: https://doi.org/10.1007/978-3-642-21557-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21556-8
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