Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Emulating the Prospector Expert System with a raster GIS
Computers & Geosciences
Practical neural network recipes in C++
Practical neural network recipes in C++
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
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
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Editorial: Geocomputation of mineral exploration targets
Computers & Geosciences
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Support vector machine for multi-classification of mineral prospectivity areas
Computers & Geosciences
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In this paper, we describe three Bayesian classifiers for mineral potential mapping: (a) a naive Bayesian classifier that assumes complete conditional independence of input predictor patterns, (b) an augmented naive Bayesian classifier that recognizes and accounts for conditional dependencies amongst input predictor patterns and (c) a selective naive classifier that uses only conditionally independent predictor patterns. We also describe methods for training the classifiers, which involves determining dependencies amongst predictor patterns and estimating conditional probability of each predictor pattern given the target deposit-type. The output of a trained classifier determines the extent to which an input feature vector belongs to either the mineralized class or the barren class and can be mapped to generate a favorability map. The procedures are demonstrated by an application to base metal potential mapping in the proterozoic Aravalli Province (western India). The results indicate that although the naive Bayesian classifier performs well and shows significant tolerance for the violation of the conditional independence assumption, the augmented naive Bayesian classifier performs better and exhibits finer generalization capability. The results also indicate that the rejection of conditionally dependent predictor patterns degrades the performance of a naive classifier.