Partial orientation and local structural learning of causal networks
for prediction
Jianxin Yin, You Zhou, Changzhang Wang, Ping He, Cheng Zheng, and Zhi
Geng; JMLR WC&P 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.
