Napredna pretraga

Pregled bibliografske jedinice broj: 1021188

Automated Decision Modeling with DMN and BPMN: A Model Ensemble Approach


Simić, Srđan Daniel; Tanković, Nikola; Etinger, Darko
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)


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
Minhen, Njemačka, 16-18.09.2019

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

Citati