Pregled bibliografske jedinice broj: 1021188
Automated Decision Modeling with DMN and BPMN: A Model Ensemble Approach
Automated Decision Modeling with DMN and BPMN: A Model Ensemble Approach // Human Systems Engineering and Design II. IHSED 2019. Advances in Intelligent Systems and Computing, vol 1026. / Ahram T. ; Karwowski W. ; Pickl S. ; Taiar R. (ur.).
Cham: Springer, 2020. str. 789-794 doi:10.1007/978-3-030-27928-8_120 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1021188 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automated Decision Modeling with DMN and BPMN: A
Model Ensemble Approach
Autori
Simić, Srđan Daniel ; Tanković, Nikola ; Etinger, Darko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Human Systems Engineering and Design II. IHSED 2019. Advances in Intelligent Systems and Computing, vol 1026.
/ Ahram T. ; Karwowski W. ; Pickl S. ; Taiar R. - Cham : Springer, 2020, 789-794
ISBN
978-3-030-27928-8
Skup
IHSED 2019: Human Systems Engineering and Design II
Mjesto i datum
München, Njemačka, 16.09.2019. - 18.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Machine learning ; Automated decision making ; White-box models
Sažetak
Plethora of available heterogeneous transactional data and recent advancements in machine learning are the key forces that enable the development of complex algorithms that can reach human-level performance on an increasing number of tasks. Given the non-linear structure composed of many layers of computation, these highly accurate models are usually applied in a black-box manner: without a deeper understanding of their inner mechanisms. This hinders the transparency of the decision-making process and can often incorporate hidden decision biases which are potentially present in the data. We propose a framework for generating decision-making models conforming to Decision Model & Notation standard based on complexity-reducing techniques. An ensemble of decision-tree classifiers in a layered architecture is proposed to control the bias- variance trade-off. We have evaluated the performance of the proposed method on several publicly available data-sets tightly related to socially sensitive decision-making.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Sveučilište Jurja Dobrile u Puli