Using machine learning techniques to interpret results from discrete event simulation
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Simulation Data Analysis Using Fuzzy Graphs
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Mining Characteristic Rules for Understanding Simulation Data
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Proceedings of the 38th conference on Winter simulation
Environmental Modelling & Software
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
Environmental Modelling & Software
Computers and Electronics in Agriculture
Environmental Modelling & Software
Environmental Modelling & Software
A distance-based approach for action recommendation
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Modelling in environmental sciences is becoming increasingly complex because ever-increasing numbers of processes are combined, thus making model-based decision aids both more relevant but more difficult to develop. Our approach focused on water quality and aimed to identify spatial tree patterns that represent surface flow and pollutant pathways from plot to plot involved in water pollution by herbicides. First, a simulation model predicted herbicide transfer rate, the proportion of applied herbicide that reaches water courses, based on the spatial and temporal distribution of weed-control activities. These predictions were used as a set of learning examples for symbolic learning techniques to induce rules based on qualitative and quantitative attributes and explain two classes of transfer rate. In this study we compared two automatic symbolic learning techniques applied to a set of simulations: (1) a relational-inductive method using the inductive logic programming (ILP) approach to induce spatial tree patterns; and (2) an attribute-value method to induce aggregated attributes of the trees. Twenty-eight and thirty-three rules were learnt by the ILP and attribute-value approaches which explained 81% and 88% of the examples, respectively. The ILP approach provided relevant indicators of plot-to-plot connectivity. The integrated attribute-value approach is simpler and quicker, but the ILP patterns are more useful for stakeholders.