On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Machine Learning - Special issue on inductive transfer
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Representing the behaviour of supervised classification learning algorithms by Bayesian networks
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Investigation and Reduction of Discretization Variance in Decision Tree Induction
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
A review of feature selection techniques in bioinformatics
Bioinformatics
Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Inference and Learning in Multi-dimensional Bayesian Network Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On learning algorithm selection for classification
Applied Soft Computing
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
Support vector machine approach for longitudinal dispersion coefficients in natural streams
Applied Soft Computing
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Environmental Modelling & Software
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Information theory and classification error in probabilistic classifiers
DS'06 Proceedings of the 9th international conference on Discovery Science
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
Good practice in Bayesian network modelling
Environmental Modelling & Software
Multilabel classifiers with a probabilistic thresholding strategy
Pattern Recognition
Information Sciences: an International Journal
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A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of 'state-of-the-art' uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs.