@InProceedings{yin08a,
  title = {Partial orientation and local structural learning of causal networks for prediction},
  author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and  <NAME>},
  pages = {93--105},
  abstract = {For a prediction problem of a given target feature in a large causal network  under external interventions, we propose in this paper two partial orientation  and local structural learning (POLSL) approaches, Local-Graph and PCD-by-PCD  (where PCD denotes Parents, Children and some Descendants). The POLSL approaches  are used to discover the local structure of the target and to orient edges   connected to the target without discovering a global causal network. Thus  they can greatly reduce computational complexity of structural learning and  improve power of statistical tests. This approach is stimulated by the challenge  problems proposed in IEEE World Congress on Computational Intelligence (WCCI2008)  competition workshop. For the cases with and without external interventions,  we select different feature sets to build prediction models. We apply the  L1 penalized logistic regression model to the prediction. For the case with  noise and calibrant features in microarray data, we propose a two-stage filter  to correct global and local patterns of noise.},
  pdf = {http://jmlr.org/proceedings/papers/v3/yin08a/yin08a.pdf},
}
