A variable selection package driving Netica with Python

• Published in: Environmental modelling & software : with environment data news Vol. 115; pp. 1 - 5
• Main Authors: Beuzen, Tomas, Simmons, Joshua
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2019; Elsevier Science Ltd
• Subjects: artificial intelligence; Bayesian analysis; Bayesian theory; Computer programs; computer software; Environment models; Learning algorithms; Machine learning; Mathematical models; Probabilistic methods; Reptiles & amphibians; Software; Source code
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2019.01.018

Bayesian Networks (BNs) are useful methods of probabilistically modelling environmental systems. BN performance is sensitive to the number of variables included in the model framework. The selection of the optimum set of variables to include in a BN (“variable selection”) is therefore a key part of the BN modelling process. While variable selection is an issue dealt with in the wider BN and machine learning literature, it remains largely absent from environmental BN applications to date, due in large part to a lack of software designed to work with available BN packages. CVNetica_VS is an open-source Python module that extends the functionality of Netica, a commonly used commercial BN software package, to perform variable selection. CVNetica_VS uses wrapper-based variable selection and cross-validation to search for the optimum variable set to use in a BN. The software will aid in objectifying and automating the development of BNs in environmental applications. •Bayesian Networks (BNs) are useful methods of probabilistically modelling environmental systems.•The process of variable selection aids in developing parsimonious and skilful BNs.•CVNetica_VS is a python tool for performing variable selection in BNs.•CVNetica_VS and the variable selection process are demonstrated with a BN that models coastal erosion.