| Title | Improving Classification Trustworthiness in Random Forests |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | de Biase, Maria Stella, Marulli, Fiammetta, Verde, Laura, Marrone, Stefano |
| Conference Name | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
| Keywords | Bayes methods, Bayesian networks, Classification Trustworthiness, Clinical Decision Support Systems, composability, Computer crime, Conferences, machine learning algorithms, Medical services, pubcrawl, random forests, reliability, trustworthiness |
| Abstract | Machine learning algorithms are becoming more and more widespread in industrial as well as in societal settings. This diffusion is starting to become a critical aspect of new software-intensive applications due to the need of fast reactions to changes, even if temporary, in data. This paper investigates on the improvement of reliability in the Machine Learning based classification by extending Random Forests with Bayesian Network models. Such models, combined with a mechanism able to adjust the reputation level of single learners, may improve the overall classification trustworthiness. A small example taken from the healthcare domain is presented to demonstrate the proposed approach. |
| DOI | 10.1109/CSR51186.2021.9527939 |
| Citation Key | de_biase_improving_2021 |
Improving Classification Trustworthiness in Random Forests