Abstract
This paper proposes a t-test decision model for context aware classifier combination scheme based on the cascade of classifier selection and fusion. In the proposed scheme, system working environment is learned and the environmental context is identified. Best selection is applied to the environment context where one classifier strongly dominates the other. In the remaining context, fusion of multiple classifiers is applied. The decision of best selection or fusion is made using t-test decision model. Fusion methods namely Cosine based identify and Euclidian identify. In the proposed scheme, we are modeling for t-test based combination system. A group of classifiers are assigned to each environmental context in prior. Then the decision of fusion of more than one classifiers or selecting best classifier is made using proposed t-test decision model.
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References
Yau, S., Wang, Y., Karim, F.: Developing Situation-Awareness in Middleware for Ubicomp Environments. In: Proc. 26th Int’l Computer Software and Applications Conference (COMPSAC 2002), pp. 233–238 (2002)
Yau, S., Karim, F., Wang, Y., Wang, B., Gupta, S.: Reconfigurable Context-Sensitive Middleware for Pervasive Computing. IEEE Pervasive Computing, 1(3), 33–40 (2002)
Kuncheva, L.I.: Switching Between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Transactions on Systems, Man, and Cybernetics - part B: cybernetics 32(2), 146–156 (2002)
Kuncheva, L.I.: A Theoretical Study on Six Classifier Fusion Strategies. IEEE S. on PAMIÂ 24(2) (2002)
Huang, Y.S., Suen, C.Y.: A Method of Combining Multiple Classifiers—A Neural Network Approach. In: Proc. 12th Int’l Conf. Pattern Recognition
Nam, Y., Rhee, P.K.: An Efficient Face Recognition for Variant Illumination Condition. In: ISPACS 2005, vol. 1, pp. 111–115 (2004)
Nam, M.Y., Rhee, P.K.: A Novel Image Preprocessing by Evolvable Neural Network. LNCS (LNAI), vol. 3214, vol. 3, pp. 843–854. Springer, Heidelberg (2004)
Kuncheva, L.I., Jain, L.C.: Designing classifier fusion systems by genetic algorithms. IEEE Transactions on Evolutionary Computation 4(4), 327–336 (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Nam, M.Y., Bayarsaikhan, B., Sedai, S., Rhee, P.K. (2006). T-Test Model for Context Aware Classifier. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_47
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DOI: https://doi.org/10.1007/11881070_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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