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

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Hybrid Intelligence for Social Networks

Abstract

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.

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Author information

Authors and Affiliations

  1. Raiffeisen Bank Austria, Petrinjska 59, Zagreb, Croatia

    Goran Klepac

  2. IN2Data, Marohnićeva 1/1, Zagreb, Croatia

    Robert Kopal & Leo Mršić

Authors
  1. Goran Klepac
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  2. Robert Kopal
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  3. Leo Mršić
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Corresponding author

Correspondence to Goran Klepac .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Dyal Singh College, University of Delhi, Delhi, India

    Hema Banati

  2. Dept. of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  3. Dept. of Electrical & Electronics Engineering, Amity University, Noida, Uttar Pradesh, India

    Ashish Mani

  4. Network Design and Research Center, Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

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Klepac, G., Kopal, R., Mršić, L. (2017). Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance. In: Banati, H., Bhattacharyya, S., Mani, A., Köppen, M. (eds) Hybrid Intelligence for Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-65139-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-65139-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65138-5

  • Online ISBN: 978-3-319-65139-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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