Ekstrakcija pravil iz globokih nevronskih mrežMatjašec, Urška (Avtor) Sadikov, Aleksander (Mentor) Jamnik, Mateja (Komentor) Shams, Zohreh (Komentor) explainability of neural networksdeep feedforward neural networksrule extractiondecision rulesdecompositional algorithmDespite their high accuracy, deep neural networks’ main disadvantage is their lack of transparency, interpretability and explainability. This has prevented them from being widely used in safety and security critical systems, for example, healthcare. There have been many attempts to interpret neural networks via rule extraction. However, the existing algorithms either do not take into account the entire structure of the network, are not applicable on deep neural networks, or are very time and memory expensive. This thesis introduces a scalable, decompositional rule extraction algorithm, which can extract simple, easy to understand IF-THEN rules from deep neural networks and can deal with the multi-class classification problems. The extracted rules approximate the network's behaviour and explain its output layer in terms of the input features. The algorithm was applied in the healthcare domain, where explainability is crucial. It was tested on a data set of breast cancer patients (METABRIC), and evaluated on two tasks of binary and multi-class classification problems. The algorithm's performance was compared to two baselines: pedagogical C5.0 and the decompositional algorithm DeepRED. As expected, the pedagogical baseline outperformed both decompositional algorithms in time and memory complexity, number of extracted rules and their average length. However, our algorithm provided more accurate rules with a higher level of fidelity than the pedagogical baseline. Our algorithm also outperformed the decompositional baseline DeepRED in all perspectives.20202020-08-20 08:15:13Magistrsko delo/naloga118104UDK: 004.42VisID: 107817COBISS_ID: 25950723sl