Partial orientation and local structural learning of causal networks for prediction

Jianxin Yin, You Zhou, Changzhang Wang, Ping He, Cheng Zheng, Zhi Geng
Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, PMLR 3:93-105, 2008.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v3-yin08a, title = {Partial orientation and local structural learning of causal networks for prediction}, author = {Yin, Jianxin and Zhou, You and Wang, Changzhang and He, Ping and Zheng, Cheng and Geng, Zhi}, booktitle = {Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008}, pages = {93--105}, year = {2008}, editor = {Guyon, Isabelle and Aliferis, Constantin and Cooper, Greg and Elisseeff, André and Pellet, Jean-Philippe and Spirtes, Peter and Statnikov, Alexander}, volume = {3}, series = {Proceedings of Machine Learning Research}, address = {Hong Kong}, month = {03--04 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v3/yin08a/yin08a.pdf}, url = {http://proceedings.mlr.press/v3/yin08a.html}, 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.} }
Endnote
%0 Conference Paper %T Partial orientation and local structural learning of causal networks for prediction %A Jianxin Yin %A You Zhou %A Changzhang Wang %A Ping He %A Cheng Zheng %A Zhi Geng %B Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 %C Proceedings of Machine Learning Research %D 2008 %E Isabelle Guyon %E Constantin Aliferis %E Greg Cooper %E André Elisseeff %E Jean-Philippe Pellet %E Peter Spirtes %E Alexander Statnikov %F pmlr-v3-yin08a %I PMLR %P 93--105 %U http://proceedings.mlr.press/v3/yin08a.html %V 3 %X 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.
RIS
TY - CPAPER TI - Partial orientation and local structural learning of causal networks for prediction AU - Jianxin Yin AU - You Zhou AU - Changzhang Wang AU - Ping He AU - Cheng Zheng AU - Zhi Geng BT - Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 DA - 2008/12/31 ED - Isabelle Guyon ED - Constantin Aliferis ED - Greg Cooper ED - André Elisseeff ED - Jean-Philippe Pellet ED - Peter Spirtes ED - Alexander Statnikov ID - pmlr-v3-yin08a PB - PMLR DP - Proceedings of Machine Learning Research VL - 3 SP - 93 EP - 105 L1 - http://proceedings.mlr.press/v3/yin08a/yin08a.pdf UR - http://proceedings.mlr.press/v3/yin08a.html AB - 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. ER -
APA
Yin, J., Zhou, Y., Wang, C., He, P., Zheng, C. & Geng, Z.. (2008). Partial orientation and local structural learning of causal networks for prediction. Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, in Proceedings of Machine Learning Research 3:93-105 Available from http://proceedings.mlr.press/v3/yin08a.html.

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