{"title":"Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes","authors":"Marcin Perzyk, Artur Soroczynski","volume":37,"journal":"International Journal of Industrial and Manufacturing Engineering","pagesStart":18,"pagesEnd":25,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/7119","abstract":"General requirements for knowledge representation in\r\nthe form of logic rules, applicable to design and control of industrial\r\nprocesses, are formulated. Characteristic behavior of decision trees\r\n(DTs) and rough sets theory (RST) in rules extraction from recorded\r\ndata is discussed and illustrated with simple examples. The\r\nsignificance of the models- drawbacks was evaluated, using\r\nsimulated and industrial data sets. It is concluded that performance of\r\nDTs may be considerably poorer in several important aspects,\r\ncompared to RST, particularly when not only a characterization of a\r\nproblem is required, but also detailed and precise rules are needed,\r\naccording to actual, specific problems to be solved.","references":"[1] M. Shahbaz, M. Srinivas, J. A. Harding and M. Turner, \u00d4\u00c7\u00d7Product design\r\nand manufacturing process improvement using association rules\", Proc\r\nInst Mech Eng Part B J Eng Manuf, vol. 220, no. 2, pp. 243-254, 2006.\r\n[2] A. Kusiak, \"Data mining: manufacturing and service applications\",\r\nInternational Journal of Production Research, 2006, vol. 44, no. 18-19,\r\npp. 4175-4191, September 2006.\r\n[3] J.A. Harding, M. Shahbaz, M. Srinivas and A. Kusiak, A. Data mining in\r\nmanufacturing: A review. J Manuf Sci Eng Trans ASME, 2006, 128(4),\r\n969-976, November 2006.\r\n[4] K. Wang, \"Applying data mining to manufacturing: The nature and\r\nimplications\", J Intell Manuf, vol. 18. no.4, pp. 487-495, August 2007.\r\n[5] A. Mahl and R. Krikler, \"Approach for a rule based system for capturing\r\nand usage of knowledge in the manufacturing industry\", J Intell Manuf,\r\nvol. 18, pp. 519-526, July 2007.\r\n[6] K. F. Tsang, H. C. W.Lau, and S. K. Kwok, \"Development of a data\r\nmining system for continual process quality improvement\", Proc Inst\r\nMech Eng Part B J Eng Manuf, vol. 221, no. 2, pp. 179-193, 2007.\r\n[7] C. H. Dagli and H. C. Lee, \"Engineering smart data mining systems for\r\ninternet aided design and manufacturing\", Int J Smart Eng Syst Design,\r\nvol. , no. 4, pp. 217-225, 2001.\r\n[8] W. C. Chen, S. S. Tseng, K. R. Hsiao and C. C. Liu, \"A data mining\r\nproject for solving low-yield situations of semiconductor\r\nmanufacturing\", in IEEE Int Symp Semicond Manuf Conf Proc, Boston,\r\n2004, pp. 129-134.\r\n[9] R. S. Chen, R. C. Wu, and C. C. Chang, \"Using data mining technology\r\nto design an intelligent CIM system for IC manufacturing\", in Proc.\r\nSixth Int. Conf. Softw. Eng. Atif. Intell. Netw. Parallel\/Distr. Comput.\r\nSelf-Assemb. Wireless Netw., SNPD\/SAWN 2005, Towson, MD, USA,\r\n2005, pp. 70-75.\r\n[10] J. Hur, H. Lee, F. G. Baek, \"An intelligent manufacturing process\r\ndiagnosis system using hybrid data mining\", Lect. Notes Comput. Sci.,\r\nvol. 4065 LNAI, pp. 561-575, July 2006.\r\n[11] M. Perzyk, R. Biernacki and J. Kozlowski, \"Data mining in\r\nmanufacturing: significance analysis of process parameters\", Proc Inst\r\nMech Eng Part B J Eng Manuf, vol. 222, no. 11, pp. 1503-1511, 2008.\r\n[12] A. Kusiak and C. Kurasek, \"Data mining of printed-circuit board\r\ndefects\", IEEE Trans Rob Autom, vol. 17, no. 2, pp. 191-196, April\r\n2001.\r\n[13] T. A. Etchells and P. J. G. Lisboa, \"Orthogonal Search-Based Rule\r\nExtraction (OSRE) for Trained Neural Networks: A Practical and\r\nEfficient Approach\", IEEE Trans Neural Networks, vol. 17, no. 2, pp.\r\n374-384, Match 2006.\r\n[14] R. K. Brouwer, \"Fuzzy rule extraction from a feed forward neural\r\nnetwork by training a representative fuzzy neural network using gradient\r\ndescent\", in Proc IEEE Int Conf Ind Technol, Hammamet, Tunisia,\r\n2004, pp. 1168-1172.\r\n[15] W. Duch, R., Adamczak and K. Grabczewski, \"A new methodology of\r\nextraction, optimization and application of crisp and fuzzy logical rules\",\r\nIEEE Trans Neural Networks, vol. 12, no. 2, pp. 277-306, March 2001.\r\n[16] S. H. Huang and H. Xing, \"Extract intelligible and concise fuzzy rules\r\nfrom neural networks\", Fuzzy Sets Syst, vol. 132, no. 2, pp. 233-243,\r\nDecember 2002.\r\n[17] H. Huang and D. Wu, \"Product quality improvement analysis using data\r\nmining: A case study in ultra-precision manufacturing industry\", Lect.\r\nNotes Comput. Sci., vol. 3614 LNAI, pp. 566-580, 2006.\r\n[18] L. Rokach, O. Maimon, \"Data mining for improving the quality of\r\nmanufacturing: a feature set decomposition approach\", J Intell Manuf,\r\nvol. 17, no. 3, pp. 285-299, June 2006.\r\n[19] D. Koonce, C. H. Fang and S. C. Tsai, \"Data mining tool for learning\r\nfrom manufacturing systems\", Comput Ind Eng, vol. 33, no. 1-2, pp. 27-\r\n30, Oct 1997.\r\n[20] T. L. Tseng, M. C. Jothishankar, T. Wu, G. Xing and F. Jiang,\r\n\"Applying data mining approaches for defect diagnosis in manufacturing\r\nindustry\", IIE Annual Conference and Exhibition, Houston, 2004, pp.\r\n1441-1447.\r\n[21] L. Shen, F. E. H. Tay, L. Qu and Y. Shen, \"Fault diagnosis using Rough\r\nSets Theory\", Computers in Industry, vol. 43, no. 1, pp. 61-72, August\r\n2000.\r\n[22] H. Sadoyan, A. Zakarian and P. Mohanty, \"Data mining algorithm for\r\nmanufacturing process control\", Int J Adv Manuf Technol, vol. 28, no.\r\n3-4, pp. 342-350, March 2006.\r\n[23] M. Perzyk and A. Kochanski, \"Prediction of ductile cast iron quality by\r\nartificial neural networks\", J Mater Proc Technol, vol. 109, no. 3, pp.\r\n305-307, February 2001.\r\n[24] M. Perzyk and A. Soroczynski, \"Comparison of selected tools for\r\ngeneration of knowledge for foundry production\", Archives of Foundry\r\nEngineering, vol. 8, no. 4, p. 263-266, December 2008.\r\n[25] J. Stefanowski and D. Vanderpooten, \"Induction of decision rules in\r\nclassification and discovery-oriented perspectives\", Int J Intell Syst, vol.\r\n16, no. 1, pp. 13-27, January 2001.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 37, 2010"}