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Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance


Klepac, Goran; Kopal, Robert; Mršić, Leo
Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance // Hybrid Intelligence for Social Networks / Banati, Hema ; Bhattacharyya, Siddhartha ; Mani, Ashish ; Koppen, Mario (ur.).
Cham: Springer International Publishing, 2017. str. 25-45 doi:10.1007/978-3-319-65139-2_2


Naslov
Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance

Autori
Klepac, Goran ; Kopal, Robert ; Mršić, Leo

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Hybrid Intelligence for Social Networks

Urednik/ci
Banati, Hema ; Bhattacharyya, Siddhartha ; Mani, Ashish ; Koppen, Mario

Izdavač
Springer International Publishing

Grad
Cham

Godina
2017

Raspon stranica
25-45

ISBN
978-3-319-65139-2

Ključne riječi
Social Network Metrics, Fuzzy Expert System , Bayesian Network, Data Science

Sažetak
Basic parameters for social network analysis comprise social network common metrics. There are numerous social network metrics. During the data analysis stage, the analyst combines different metrics to search for interesting patterns. This process can be exhaustive with regard to the numerous potential combinations and how we can combine different metrics. In addition, other, non-network measures can be observed together with social network metrics. This chapter illustrates the proposed methodology for fraud detection systems in the insurance industry, where the fuzzy expert system and the Bayesian network was the basis for an analytical platform, and social network metrics were used as part of the solution to improve performance. The solution developed shows the importance of integrated social network metrics as a contribution towards better accuracy in fraud detection. This chapter describes a case study with a description of the phases of the process, from data preparation, attribute selection, model development to predictive power evaluation. As a result, from the empirical result, it is evident that the use of social network metrics within Bayesian networks and fuzzy expert systems significantly increases the predictive power of the model.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove
Visoko učilište Algebra, Zagreb

Profili:

Avatar Url Goran Klepac (autor)

Avatar Url Leo Mršić (autor)

Avatar Url Robert Kopal (autor)

Citiraj ovu publikaciju

Klepac, Goran; Kopal, Robert; Mršić, Leo
Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance // Hybrid Intelligence for Social Networks / Banati, Hema ; Bhattacharyya, Siddhartha ; Mani, Ashish ; Koppen, Mario (ur.).
Cham: Springer International Publishing, 2017. str. 25-45 doi:10.1007/978-3-319-65139-2_2
Klepac, G., Kopal, R. & Mršić, L. (2017) Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance. U: Banati, H., Bhattacharyya, S., Mani, A. & Koppen, M. (ur.) Hybrid Intelligence for Social Networks. Cham, Springer International Publishing, str. 25-45 doi:10.1007/978-3-319-65139-2_2.
@inbook{inbook, year = {2017}, pages = {25-45}, DOI = {10.1007/978-3-319-65139-2\_2}, keywords = {Social Network Metrics, Fuzzy Expert System , Bayesian Network, Data Science}, doi = {10.1007/978-3-319-65139-2\_2}, isbn = {978-3-319-65139-2}, title = {Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance}, keyword = {Social Network Metrics, Fuzzy Expert System , Bayesian Network, Data Science}, publisher = {Springer International Publishing}, publisherplace = {Cham} }

Časopis indeksira:


  • Scopus


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