EBNO: Evolution of cost‐sensitive Bayesian networks (2019)
Journal Article
Nashnush, E., & VADERA, S. (2020). EBNO: Evolution of cost‐sensitive Bayesian networks. Expert Systems, 37(3), e12495. https://doi.org/10.1111/exsy.12495

The last decade has seen an increase in the attention paid to the development of cost sensitive learning algorithms that
aim to minimize misclassification costs while still maintaining accuracy. Most of this attention has been on cost sensitive
dec... Read More about EBNO: Evolution of cost‐sensitive Bayesian networks.

Learning cost-sensitive Bayesian networks via direct and indirect methods (2016)
Journal Article
Nashnush, E., & Vadera, S. (2016). Learning cost-sensitive Bayesian networks via direct and indirect methods. Integrated Computer-Aided Engineering, 24(1), 17-26. https://doi.org/10.3233/ica-160514

Cost-Sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree l... Read More about Learning cost-sensitive Bayesian networks via direct and indirect methods.

Cost-Sensitive Bayesian Network Learning Using Sampling (2014)
Book Chapter
Nashnush, E., & Vadera, S. (2014). Cost-Sensitive Bayesian Network Learning Using Sampling. In Recent Advances on Soft Computing and Data Mining (467-476). Springer. https://doi.org/10.1007/978-3-319-07692-8_44

A significant advance in recent years has been the development of cost-sensitive decision tree learners, recognising that real world classification problems need to take account of costs of misclassification and not just focus on accuracy. The litera... Read More about Cost-Sensitive Bayesian Network Learning Using Sampling.