Abstract
The paper presents classifier models in intelligent computer aided process planning (CAPP) systems. The models of simple classifiers and models of multiple classifiers are compared in order to obtain optimal classification. The models are tested on real data from an enterprise. Based on the classification models, the intelligent support system allows to create scenarios for selection of tools to manufacturing operations. Therefore the embedded models improve this selection. We present decision trees as models for classifcation in intelligent CAPP systems. The research was done for selected manufacturing operations: turning, milling and grinding. Models for milling operation were presented in detail.
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References
Bauer, E., Kohavi, R.: An empirical comparision of voting classification algorithms: bagging, boosting and variants. Machine Learning 36, 105–139 (1999)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Cios, K.J., Pedrycz, W., Swinarski, R.W.: Data mining methods for knowledge discovery. Kluwer, Dordrecht (1999)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Freund, Y.: Boosting a weak learning algorithms by majority. Information and Computation 121(2), 256–285 (1995)
Han, J., Kamber, M.: Data Mining: Concepts and techniques. Morgan Kaufmann, San Francisco (2000)
Klosgen, W., Zytkow, J.M.: Handbook of Data Mining and Knowledge Discovery. Oxford Press (2002)
Langley, P., Simon, H.A.: Fielded applications of machine learning. In: Michalski, R.S., Bratko, I., Kubat, M. (eds.) Machine learning and Data Mining, John Wiley & Sons, Chichester (1998)
Michalski, R.S., Bratko, I., Kubat, M.: Machine learning and Data Mining. John Wiley & Sons, Chichester (1998)
Quinlan, J.R.: Bagging, boosting and C4.5. In: Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725–730 (1996)
Rojek, I.: Knowledge discovery for computer aided process planning systems. Polish Journal of Environmental Studies 16(4A), 268–270 (2007); Short communication
Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197–226 (1990)
Wolpert, D.: Stacked generalisation. Neural Networks 5, 241–259 (1992)
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Rojek, I. (2009). Classifier Models in Intelligent CAPP Systems. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_32
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DOI: https://doi.org/10.1007/978-3-642-00563-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00562-6
Online ISBN: 978-3-642-00563-3
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