Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Machine Learning - Special issue on learning with probabilistic representations
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Dynamic ordering-based search algorithm for markov blanket discovery
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
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Learning of Markov blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.