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Pregled bibliografske jedinice broj: 1084377

Hybrid approach based on multi-criteria decision making and machine learning in the public sector


Đurek, Valentina; Oreški, Dijana; Kadoić, Nikola
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

Profili:

Avatar Url Dijana Oreški (autor)

Avatar Url Nikola Kadoić (autor)


Citiraj ovu publikaciju:

Đurek, Valentina; Oreški, Dijana; Kadoić, Nikola
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)
Đurek, V., Oreški, D. & Kadoić, N. (2020) Hybrid approach based on multi-criteria decision making and machine learning in the public sector. U: Čeh Časni, A. & Arnerić, J. (ur.)Book of abstracts, 17th International conference on operational research KOI 2020.
@article{article, author = {\DJurek, Valentina and Ore\v{s}ki, Dijana and Kadoi\'{c}, Nikola}, year = {2020}, pages = {38-39}, keywords = {Public sector, SNAP, composite index, artificial neural network, efficiency}, title = {Hybrid approach based on multi-criteria decision making and machine learning in the public sector}, keyword = {Public sector, SNAP, composite index, artificial neural network, efficiency}, publisherplace = {\v{S}ibenik, Hrvatska} }
@article{article, author = {\DJurek, Valentina and Ore\v{s}ki, Dijana and Kadoi\'{c}, Nikola}, year = {2020}, pages = {38-39}, keywords = {Public sector, SNAP, composite index, artificial neural network, efficiency}, title = {Hybrid approach based on multi-criteria decision making and machine learning in the public sector}, keyword = {Public sector, SNAP, composite index, artificial neural network, efficiency}, publisherplace = {\v{S}ibenik, Hrvatska} }




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