Pregled bibliografske jedinice broj: 1084377
Hybrid approach based on multi-criteria decision making and machine learning in the public sector
Hybrid approach based on multi-criteria decision making and machine learning in the public sector // Book of abstracts, 17th International conference on operational research KOI 2020 / Čeh Časni, Anita ; Arnerić, Josip (ur.).
Zagreb, 2020. str. 38-39 (predavanje, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 1084377 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Hybrid approach based on multi-criteria decision
making and machine learning in the public sector
Autori
Đurek, Valentina ; Oreški, Dijana ; Kadoić, Nikola
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Book of abstracts, 17th International conference on operational research KOI 2020
/ Čeh Časni, Anita ; Arnerić, Josip - Zagreb, 2020, 38-39
Skup
18th International Conference on Operational Research (KOI 2020)
Mjesto i datum
Šibenik, Hrvatska, 23.09.2020. - 25.09.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Public sector ; SNAP ; composite index ; artificial neural network ; efficiency
Sažetak
In the process of making decisions in the public sector, various methods can be used, but practitioners face many problems in the implementation: (1) the methods are very complex, (2) the process is time-consuming, and (3) there is a lack of the experts that should be involved, and (4) users are limited in resources. In this paper, we are proposing a new, hybrid approach to make decisions in the public sector. The approach combines methods from the areas of multi-criteria decision making (SNAP and composite index) and machine learning (artificial neural network. Some of many benefits of the new approach are: (1) once created, the decision model can be reusable without need that users know the details of the methodology, (2) the proposed model considers interactions between elements of the model, without issues in stochasticity of problem matrix (like in ANP), (3) since the neural network model contains experts’ knowledge, it is not needed that in future implementations of the model we repeatedly include new (or same) experts again ; and (4) the model contains long-term knowledge about the criteria and alternatives of the problem and that is often a request in public sector problems.
Izvorni jezik
Engleski
POVEZANOST RADA
Ustanove:
Fakultet organizacije i informatike, Varaždin