Title
Author
Year
Volume
:
2024
Vol. 1
no. 1
Vol. 2
2023
Vol. 1
no. 1
no. 2
2022
Vol. 1
no. 1
2021
Vol. 1
no. 1
2019
Vol. 1
no. 1
no. 2
2013
Vol. 1
no. 1
Vol. 2
no. 1
2011
Vol. 1
no. 1
Vol. 2
no. 1
2010
Vol. 1
no. 1
no. 2
Vol. 2
Vol. 0
Vol. 1
Improved Bayesian Network Structure Learning for Breast Cancer Prognosis
Pages
:
68-76
Farzana Kabir Ahmad, Safaai Deris, Nor Hayati Othman
Structure learning of Bayesian networks is a well-researched but computationally and NP-hard task. We present an algorithm that integrates a low-order conditional independence approach for learning structures of Bayesian networks. Our algorithm also makes use of basic Bayesian network concepts. We show that the proposed algorithm is capable of handling networks with a large number of variables and small sample size in the case of microarray data analysis. We present the applicability of the proposed algorithm on breast cancer data sets and also compare its performance and computational efficiency with full-order conditional independence method. The experimental results show that our method can efficiently and accurately identify complex network structures from data.
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