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
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Introduction to artificial life
Introduction to artificial life
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
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
Evolutionary Rule Mining in Time Series Databases
Machine Learning
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Continuation processes in chemical and/or biotechnical plantsalways generate a large amount of time series data. However, sinceconventional process models are described as a set of controlmodels, it is difficult to explain the complicated and active plantbehaviours. Based on the background, this paper proposes a novelmethod to develop a process response model from continuoustime-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)detecting outliers. The main contribution of the research is toestablish a method to mine a set of meaningful control rules fromLearning Classifier System (LCS) using the Minimum DescriptionLength (MDL) criteria and Tabu search method. The proposed methodhas been applied to an actual process of a biochemical plant andhas shown the validity and the effectiveness.