Summary
Currently, the design of the signal processing and recognition architecture for intelligent sensor systems is still a tedious and labor-intensive task. Optimization techniques, e.g., from gradient descent, stochastic search or evolutionary computation are available to accelerate and automate this procedure. However, appropriate assessment or cost functions are required. This paper presents effective state-of-the-art techniques and compares them to two novel, salient modifications. These are a normalized compactness measure, which has salient properties as it is non-parametric, easy-to-use, fine-grained, competitive performing, and the sum-volumetric k-NN classifier. The aims of this paper are to compare the computation complexities, discriminant properties or capabilities, and sensitivity to control parameter. The methods will be described and compared for the task of dimension reduction by feature selection. Achieved results underpin their saliency for general optimized sensor system design.
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References
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. of Machine Learning Research 3, 1157–1182 (2003)
Gutierrez-Osuna, R., Nagle, H.T.: A method for evaluating data- preprocessing techniques for odor classification with an array of gas sensors. IEEE Trans. on Systems, Man and Cybernetics B 29(5), 626–632 (1999)
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality Reduction Using Genetic Algorithms. IEEE Trans. on Evolutionary Computation 4(2), 164–171 (2000)
Iswandy, K., König, A., Fricke, T., Baumbach, M., Schuetze, A.: Towards Automated Configuration of Multi-Sensor Systems Using Evolutionary Computation - A Method and a Case Study. J. Computational and Theoretical Nanoscience 2(4), 574–582 (2005)
Iswandy, K., König, A.: Feature selection with acquisition cost for optimizing sensor system design. Adv. Radio Sci. 4, 135–141 (2006)
König, A.: Interactive visualization and analysis of hierarchical neural projections for data mining. IEEE Trans. on Neural Networks 11(3), 615–624 (2000)
König, A., Mayr, C., Bormann, T., Klug, C.: Dedicated Implementation of Embedded Vision Systems Employing Low-Power Massively Parallel Feature Computation. In: Proc. of the 3rd VIVA-Workshop on Low-Power Information Processing, pp. 1–8 (2002)
König, A., Gratz, A.: Advanced methods for the analysis of Semiconductor Manufacturing Process Data. In: Pal, N.R., Jain, L.C. (eds.) Advanced Techniques in Knowledge Discovery and Data Mining, pp. 27–74. Springer, Heidelberg (2005)
Marques de Sá, J.P.: Pattern Recognition: Concept, Methods and Applications. Springer, Heidelberg (2001)
Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn. Academic Press Limited, London (1990)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of IEEE Int. Conf. on Neural Networks (ICNN), vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: A Discrete Binary Version of The Particle Swarm Algorithm. In: Proc. of Conf. on System, Man, and Cybernetics, pp. 4104–4109 (1997)
Agrafiotis, D.K., Cedenõ, R.C.: Feature Selection for Structure - Activity Correlation Using Binary Particle Swarms. J. Med. Chem. 45, 1098–1107 (2002)
Wang, X., Yang, J., Peng, N., Teng, X.: Finding Minimal Rough Set Reducts with Particle Swarm Optimization. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 451–460 (2005)
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Iswandy, K., König, A. (2009). Comparison of Effective Assessment Functions for Optimized Sensor System Design. In: Avineri, E., Köppen, M., Dahal, K., Sunitiyoso, Y., Roy, R. (eds) Applications of Soft Computing. Advances in Soft Computing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88079-0_4
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DOI: https://doi.org/10.1007/978-3-540-88079-0_4
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