Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Biomass Based Weed-Crop Competitiveness Classification Using Bayesian Networks
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
International Journal of Hybrid Intelligent Systems - HIS 2007
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Engineering Applications of Artificial Intelligence
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
An integrative framework for intelligent software project risk planning
Decision Support Systems
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This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed.