A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic programming using a minimum description length principle
Advances in genetic programming
Data mining
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Introduction to artificial life
Introduction to artificial life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Evolutionary Rule Mining in Time Series Databases
Machine Learning
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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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.