Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Instance-Based Learning Algorithms
Machine Learning
Optimizing causal orderings for generating DAGs from data
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Robust Classification for Imprecise Environments
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A genetic algorithm for tuning variable orderings in Bayesian network structure learning
Eighteenth national conference on Artificial intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Information Sciences: an International Journal - Special issue: Soft computing data mining
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of Artificial Intelligence Research
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A hybrid anytime algorithm for the construction of causal models from sparse data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Linking Bayesian networks and PLS path modeling for causal analysis
Expert Systems with Applications: An International Journal
An optimization of ReliefF for classification in large datasets
Data & Knowledge Engineering
Data mining for exploring hidden patterns between KM and its performance
Knowledge-Based Systems
Diagnose the mild cognitive impairment by constructing Bayesian network with missing data
Expert Systems with Applications: An International Journal
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
Journal of Medical Systems
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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Traditionally, the task of learning Bayesian Networks (BNs) from data has been treated as a NP-Hard search problem. To overcome such difficulty in terms of computational complexity, several approximations have been designed, such as imposing a previous ordering on the domain attributes that restrict the number of Bayesian structures to be learned or using other approaches trying to reduce the state space of this problem. In this paper, we propose a simple method based on feature ranking algorithms which has low computational complexity (O(n^2), where n is the number of variables) and produces good results. We empirically demonstrate that feature ranking algorithms (namely, Chi-Squared and Information Gain) can be used to define efficient variables ordering in the BNC learning context. The proposed method can bring improvements, when using the K2 algorithm, to learn a Bayesian Network Classifier from data.