| Title | XAI Models for Quality of Experience Prediction in Wireless Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Renda, Alessandro, Ducange, Pietro, Gallo, Gionatan, Marcelloni, Francesco |
| Conference Name | 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
| Date Published | July 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-4407-1 |
| Keywords | 6G, Analytical models, B5G, Complexity theory, Decision trees, evolutionary computation, explainable artificial intelligence, Fuzzy Decision Trees, Predictive models, pubcrawl, quality of experience, Radio frequency, resilience, Resiliency, Scalability, wireless networks, xai |
| Abstract | Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability. |
| URL | https://ieeexplore.ieee.org/document/9494509 |
| DOI | 10.1109/FUZZ45933.2021.9494509 |
| Citation Key | renda_xai_2021 |
XAI Models for Quality of Experience Prediction in Wireless Networks