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Title: Knowledge Discovery with Bayesian Networks
Keywords: Causal knowledge, Bayesian networks, knowledge discovery, hypothesis verification, non-symmetrical entropy, active learning
Issue Date: 22-Jun-2009
Citation: LI GUOLIANG (2009-06-22). Knowledge Discovery with Bayesian Networks. ScholarBank@NUS Repository.
Abstract: Identification of causal knowledge is an important research topic with a long history and many challenging issues. The majority of existing approaches to causal knowledge discovery are based on statistical randomized experiments and inductive learning from observational data. This thesis proposes a three-step iterative framework for causal knowledge discovery with Bayesian networks under a manipulation criterion. Its goal is to exploit available resources, including observational data, interventional data, topological domain knowledge, and interventional experiments, to discover new causal knowledge, and minimize the number of interventional experiments required to validate the causal knowledge. The direct causal influence relationships between variables are modeled as hypotheses. Variable grouping is proposed for hypothesis generation, topological constraints are proposed to refine hypotheses, and non-symmetrical entropy is proposed to select hypotheses for verification with interventional experiments. The proposed framework and algorithms show promising results in experiments and are applicable to many domains for causal knowledge discovery, such as in reverse engineering tasks.
Appears in Collections:Ph.D Theses (Open)

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