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A novel scalable, data efficient and correct Markov boundary learning algorithm under faithfulness condition
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Sergio Rodrigues De Morais Aussem, Alexandre |
| Abstract | In this paper, we discuss a novel scalable, data efficient and correct Markov boundary learning algorithm under faithfulness condition. The latter combines the main advantages of PCMB and IAMB yet avoids some of their drawbacks. An empiric evaluation of our algorithm is provided on synthetic and real sparse databases scaling up to 139,351 variables. Our method is shown to be efficient in terms of both runtime and accuracy. |
| File Format | |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Bayesian networks Markov boundary probabilistic classification feature subset selection info Computer Science [cs] Machine Learning [cs.LG] |
| Content Type | Text |
| Resource Type | Article |