Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning on High Dimensional Data

2007 ◽  
Author(s):  
Feng Liu ◽  
Qiliang Zhu
Keyword(s):  
Bayesian Network ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Network Learning

scholarly journals Equation Chapter 1 Section 1 Differentially Private High-Dimensional Binary Data Publication via Adaptive Bayesian Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sun Lan ◽  
Jinxin Hong ◽  
Junya Chen ◽  
Jianping Cai ◽  
Yilei Wang
Keyword(s):  
Bayesian Network ◽  
Time Complexity ◽  
Binary Data ◽  
Differential Privacy ◽  
Functional Performance ◽  
High Dimensional Data ◽  
Real Life ◽  
Synthetic Dataset ◽  
High Dimensional

When using differential privacy to publish high-dimensional data, the huge dimensionality leads to greater noise. Especially for high-dimensional binary data, it is easy to be covered by excessive noise. Most existing methods cannot address real high-dimensional data problems appropriately because they suffer from high time complexity. Therefore, in response to the problems above, we propose the differential privacy adaptive Bayesian network algorithm PrivABN to publish high-dimensional binary data. This algorithm uses a new greedy algorithm to accelerate the construction of Bayesian networks, which reduces the time complexity of the GreedyBayes algorithm from O n k C m + 1 k + 2 to O n m 4 . In addition, it uses an adaptive algorithm to adjust the structure and uses a differential privacy Exponential mechanism to preserve the privacy, so as to generate a high-quality protected Bayesian network. Moreover, we use the Bayesian network to calculate the conditional distribution with noise and generate a synthetic dataset for publication. This synthetic dataset satisfies ε -differential privacy. Lastly, we carry out experiments against three real-life high-dimensional binary datasets to evaluate the functional performance.

scholarly journals A New Method for Predicting Patient Survivorship Using Efficient Bayesian Network Learning

2014 ◽  
Vol 13 ◽  
pp. CIN.S13053 ◽  
Author(s):  
Xia Jiang ◽  
Diyang Xue ◽  
Adam Brufsky ◽  
Seema Khan ◽  
Richard Neapolitan
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Molecular Taxonomy ◽  
High Dimensional Data ◽  
Prediction Method ◽  
Clinical Decision ◽  
Sensitivity Analyses ◽  
High Dimensional ◽  
Additional Information ◽  
Central Hypothesis

The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individually. EBMC_S is based on the EBMC BN algorithm, which has been shown to handle high-dimensional data. BNs have excellent architecture for decision support systems. In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional
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