Discretisation Does Affect the Performance of Bayesian Networks

• Published in: Research and Development in Intelligent Systems XXVII pp. 237 - 250
• Main Authors: Robben, Saskia, Velikova, Marina, Lucas, Peter J.F., Samulski, Maurice
• Format: Book Chapter
• Language: English
• Published: London Springer London 2011
• Subjects: Bayesian Network; Bayesian Network Model; Discretisation Technique; Link Level; Receiver Operating Characteristic Curve
• ISBN: 9780857291295; 0857291297
• DOI: 10.1007/978-0-85729-130-1_17

In this paper, we study the use of Bayesian networks to interpret breast X-ray images in the context of breast-cancer screening. In particular, we investigate the performance of a manually developed Bayesian network under various discretisation schemes to check whether the probabilistic parameters in the initial manual network with continuous features are optimal and correctly reflect the reality. The classification performance was determined using ROC analysis. A few algorithms perform better than the continuous baseline: best was the entropy-based method of Fayyad and Irani, but also simpler algorithms did outperform the continuous baseline. Two simpler methods with only 3 bins per variable gave results similar to the continuous baseline. These results indicate that it is worthwhile to consider discretising continuous data when developing Bayesian networks and support the practical importance of probabilitistic parameters in determining the network’s performance.