Abstract
Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain complicated and active plant behaviors. To uncover complex plant behaviors, this paper proposes a new method of developing a process response model from continuous time-series data. The method consists of the following phases: (1) Reciprocal correlation analysis; (2) Process response model; (3) Extraction of control rules; (4) Extraction of a workflow; and (5) Detection of outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from a Learning Classifier System using the Minimum Description Length criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown its validity and effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)
Adami, C.: Introduction to Artificial Life. Springer, NY (1998)
Mehta, M., Rissanen, J., Agrawal, R.: MDL-based decision tree pruning. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), pp. 216–221 (1995)
Yamanishi, K.: A learning criterion for stochastic rules. Machine Learning 8, 165–203 (1992)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of assosiation rules. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press and The MIT Press (1996)
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Dordrecht (2001)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. (63), 49 (1977)
Smith, S.: A learning system based on genetic adaptive algorithms. In: Ph.D thesis. University of Pittsburgh (1980)
Smith, S.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings 8th International Joint Conference on Artificial Intelligence (August 1983)
Butz, M.V., Pelikan, M., Llorà , X., Goldberg, D.E.: Extracted global structure makes local building block processing effective in XCS. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 655–662. ACM, New York (2005)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)
Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Reading (1996)
Weiss, S.M., Indurkhya, N.: Predictice Data Mining, A Practical Guide. Morgan Kaufmann Publishers, Inc., San Francisco (1997)
Berndt, D.J., Clifford, J.: Finding patterns in time series: a dynamic programming approach, 229–248 (1996)
Hetland, M.L., Saetrom, P.: Evolutionary rule mining in time series databases. Mach. Learn. 58(2-3), 107–125 (2005)
Harvey, A.C.: Time Series Models. Prentice Hall/Harvester (1993)
Stock, J.H., Watson, M.W.: A probability model of the coincident economic indicators. Working Paper 2772, National Bureau of Economic Research (November 1988)
Freitas, A.A.: Data mining and knowledge discovery with evolutionary algorithms. Springer, Heidelberg (2002)
Barry, A., Holme, J., Llora, X.: Data mining using learning classifier systems. In: Bull, L. (ed.) Applications of Learning Classifier Systems, pp. 15–67. Springer, Heidelberg (2004)
Iba, H., de Garis, H., Sato, T.: Genetic programming using a minimum description length principle. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)
Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Kovacs, T., Llorà , X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, pp. 59–79. Springer, Heidelberg (2007)
Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering 4(4), 301–316 (1992)
Hilderman, R.J., Hamilton, H.J.: Heuristic measures of interestingness. In: Proceedings of the Third European Conference on the Principles of Data Mining and Knowledge Discovery, pp. 232–241 (1999)
Quinlan, J.R.: C4.5:Programs for Machine Learning. Morgan Kaufman Publishers, Inc., San Francisco (1993)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)
Yamanishi, K., Takeuchi, J.: A unifying approach to detecting outliers and change-points from nonstationary data. In: The Eighth ACM SIGKDD(KDD2002) (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kurahashi, S., Terano, T. (2008). Technology Extraction of Expert Operator Skills from Process Time Series Data. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà , X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-88138-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88137-7
Online ISBN: 978-3-540-88138-4
eBook Packages: Computer ScienceComputer Science (R0)