Abstract
The basic contribution of this paper is the presentation of two methods that can be used to design a practical robot obstacle classification system based on data mining methods from rough set theory. These methods incorporate recent advances in rough set theory related to coping with the uncertainty in making obstacle classification decisions either during the operation of a mobile robot. Obstacle classification is based on the evaluation of data acquired by proximity sensors connected to a line-crawling robot useful in inspecting power transmission lines. A fairly large proximity sensor data set has been used as means of benchmarking the proposed classification methods, and also to facilitate comparison with other published studies of the same data set. Using 10-fold cross validated paired t-test, this paper compares the rough set classification learning method with the Waikato Environment for Knowledge Analysis (WEKA) classification learning method.
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References
Bazan J., Szczuka M., Wróblewski J., A new version of the rough set exploration system. In Proceedings of RSCTC’02, LNAI 2475. Springer-Verlag, Berlin, 2002, 397–404.
Beyer, W.H. Handbook of Tables for Probability and Statistics. CRC Press, Ohio, 1968.
Hogg R.V., Tanis E.A. Probability and Statistical Inference. Macmillan Publishing Co., New York, 1997.
Hussein, A., Dietterich, T.G. Efficient algorithms for identifying relevant features. Proc. of the 9th Canadian Conf. on AI, Vancouver, B.C., 1992, 38–45.
Mitchell, T.M. Machine Learning. McGraw-Hill, NY, 1997.
Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston, 1991.
Peters, J.F., Skowron, A., Suraj, Z., Pedrycz, W., Pizzi, N., Ramanna, S. Classification of meteorological volumetric radar data using rough set methods. Pattern Recognition Letters 24(6), 2002, 911–920.
Rosetta 1999. http://www.idi.ntnu.no/~aleks/rosetta/
RSES 2002. http://logic.mimuw.edu.pl/~rses/
WEKA 2002. http://www.cs.waikato.ac.nz/ml/weka
Witten, I.H., Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kauffman, San Francisco, 2000.
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© 2003 Springer-Verlag Berlin Heidelberg
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Peters, J.F., Ramanna, S., Szczuka, M.S. (2003). Towards a Line-Crawling Robot Obstacle Classification System: A Rough Set Approach. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_44
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DOI: https://doi.org/10.1007/3-540-39205-X_44
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