Commenced in January 2007
Paper Count: 30067
Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes
Abstract:General requirements for knowledge representation in the form of logic rules, applicable to design and control of industrial processes, are formulated. Characteristic behavior of decision trees (DTs) and rough sets theory (RST) in rules extraction from recorded data is discussed and illustrated with simple examples. The significance of the models- drawbacks was evaluated, using simulated and industrial data sets. It is concluded that performance of DTs may be considerably poorer in several important aspects, compared to RST, particularly when not only a characterization of a problem is required, but also detailed and precise rules are needed, according to actual, specific problems to be solved.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333220Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 M. Shahbaz, M. Srinivas, J. A. Harding and M. Turner, ÔÇ×Product design and manufacturing process improvement using association rules", Proc Inst Mech Eng Part B J Eng Manuf, vol. 220, no. 2, pp. 243-254, 2006.
 A. Kusiak, "Data mining: manufacturing and service applications", International Journal of Production Research, 2006, vol. 44, no. 18-19, pp. 4175-4191, September 2006.
 J.A. Harding, M. Shahbaz, M. Srinivas and A. Kusiak, A. Data mining in manufacturing: A review. J Manuf Sci Eng Trans ASME, 2006, 128(4), 969-976, November 2006.
 K. Wang, "Applying data mining to manufacturing: The nature and implications", J Intell Manuf, vol. 18. no.4, pp. 487-495, August 2007.
 A. Mahl and R. Krikler, "Approach for a rule based system for capturing and usage of knowledge in the manufacturing industry", J Intell Manuf, vol. 18, pp. 519-526, July 2007.
 K. F. Tsang, H. C. W.Lau, and S. K. Kwok, "Development of a data mining system for continual process quality improvement", Proc Inst Mech Eng Part B J Eng Manuf, vol. 221, no. 2, pp. 179-193, 2007.
 C. H. Dagli and H. C. Lee, "Engineering smart data mining systems for internet aided design and manufacturing", Int J Smart Eng Syst Design, vol. , no. 4, pp. 217-225, 2001.
 W. C. Chen, S. S. Tseng, K. R. Hsiao and C. C. Liu, "A data mining project for solving low-yield situations of semiconductor manufacturing", in IEEE Int Symp Semicond Manuf Conf Proc, Boston, 2004, pp. 129-134.
 R. S. Chen, R. C. Wu, and C. C. Chang, "Using data mining technology to design an intelligent CIM system for IC manufacturing", in Proc. Sixth Int. Conf. Softw. Eng. Atif. Intell. Netw. Parallel/Distr. Comput. Self-Assemb. Wireless Netw., SNPD/SAWN 2005, Towson, MD, USA, 2005, pp. 70-75.
 J. Hur, H. Lee, F. G. Baek, "An intelligent manufacturing process diagnosis system using hybrid data mining", Lect. Notes Comput. Sci., vol. 4065 LNAI, pp. 561-575, July 2006.
 M. Perzyk, R. Biernacki and J. Kozlowski, "Data mining in manufacturing: significance analysis of process parameters", Proc Inst Mech Eng Part B J Eng Manuf, vol. 222, no. 11, pp. 1503-1511, 2008.
 A. Kusiak and C. Kurasek, "Data mining of printed-circuit board defects", IEEE Trans Rob Autom, vol. 17, no. 2, pp. 191-196, April 2001.
 T. A. Etchells and P. J. G. Lisboa, "Orthogonal Search-Based Rule Extraction (OSRE) for Trained Neural Networks: A Practical and Efficient Approach", IEEE Trans Neural Networks, vol. 17, no. 2, pp. 374-384, Match 2006.
 R. K. Brouwer, "Fuzzy rule extraction from a feed forward neural network by training a representative fuzzy neural network using gradient descent", in Proc IEEE Int Conf Ind Technol, Hammamet, Tunisia, 2004, pp. 1168-1172.
 W. Duch, R., Adamczak and K. Grabczewski, "A new methodology of extraction, optimization and application of crisp and fuzzy logical rules", IEEE Trans Neural Networks, vol. 12, no. 2, pp. 277-306, March 2001.
 S. H. Huang and H. Xing, "Extract intelligible and concise fuzzy rules from neural networks", Fuzzy Sets Syst, vol. 132, no. 2, pp. 233-243, December 2002.
 H. Huang and D. Wu, "Product quality improvement analysis using data mining: A case study in ultra-precision manufacturing industry", Lect. Notes Comput. Sci., vol. 3614 LNAI, pp. 566-580, 2006.
 L. Rokach, O. Maimon, "Data mining for improving the quality of manufacturing: a feature set decomposition approach", J Intell Manuf, vol. 17, no. 3, pp. 285-299, June 2006.
 D. Koonce, C. H. Fang and S. C. Tsai, "Data mining tool for learning from manufacturing systems", Comput Ind Eng, vol. 33, no. 1-2, pp. 27- 30, Oct 1997.
 T. L. Tseng, M. C. Jothishankar, T. Wu, G. Xing and F. Jiang, "Applying data mining approaches for defect diagnosis in manufacturing industry", IIE Annual Conference and Exhibition, Houston, 2004, pp. 1441-1447.
 L. Shen, F. E. H. Tay, L. Qu and Y. Shen, "Fault diagnosis using Rough Sets Theory", Computers in Industry, vol. 43, no. 1, pp. 61-72, August 2000.
 H. Sadoyan, A. Zakarian and P. Mohanty, "Data mining algorithm for manufacturing process control", Int J Adv Manuf Technol, vol. 28, no. 3-4, pp. 342-350, March 2006.
 M. Perzyk and A. Kochanski, "Prediction of ductile cast iron quality by artificial neural networks", J Mater Proc Technol, vol. 109, no. 3, pp. 305-307, February 2001.
 M. Perzyk and A. Soroczynski, "Comparison of selected tools for generation of knowledge for foundry production", Archives of Foundry Engineering, vol. 8, no. 4, p. 263-266, December 2008.
 J. Stefanowski and D. Vanderpooten, "Induction of decision rules in classification and discovery-oriented perspectives", Int J Intell Syst, vol. 16, no. 1, pp. 13-27, January 2001.