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
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
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Tradeoff analysis of different Markov blanket local learning approaches
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Learning of Markov blanket can be regarded as an optimal solution to the feature selection problem. In this paper, we propose a local learning algorithm, called Breadth-First search of MB (BFMB), to induce Markov blanket (MB) without having to learn a Bayesian network first. It is demonstrated as (1) easy to understand and prove to be sound in theory; (2) data efficient by making full use of the knowledge of underlying topology of MB; (3) fast by relying on fewer data passes and conditional independent test than other approaches; (4) scalable to thousands of variables due local learning. Empirical results on BFMB, along with known Iterative Association Markov blanket (IAMB) and Parents and Children based Markov boundary (PCMB), show that (i) BFMB significantly outperforms IAMB in measures of data efficiency and accuracy of discovery given the same amount of instances available (ii) BFMB inherits all the merits of PCMB, but reaches higher accuracy level using only around 20% and 60% of the number of data passes and conditional tests, respectively, used by PCMB.