Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes
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.
[1] 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.
[2] A. Kusiak, "Data mining: manufacturing and service applications",
International Journal of Production Research, 2006, vol. 44, no. 18-19,
pp. 4175-4191, September 2006.
[3] 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.
[4] K. Wang, "Applying data mining to manufacturing: The nature and
implications", J Intell Manuf, vol. 18. no.4, pp. 487-495, August 2007.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[1] 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.
[2] A. Kusiak, "Data mining: manufacturing and service applications",
International Journal of Production Research, 2006, vol. 44, no. 18-19,
pp. 4175-4191, September 2006.
[3] 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.
[4] K. Wang, "Applying data mining to manufacturing: The nature and
implications", J Intell Manuf, vol. 18. no.4, pp. 487-495, August 2007.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:56201", author = "Marcin Perzyk and Artur Soroczynski", title = "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.", keywords = "Knowledge extraction, decision trees, rough setstheory, industrial processes.", volume = "4", number = "1", pages = "22-7", }
