Skip to main content
SpringerLink
Log in

Fuzzy Sets for Modeling Interstate Conflict

  • Chapter
  • First Online:
Militarized Conflict Modeling Using Computational Intelligence

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

  • 547 Accesses

Abstract

This chapter investigates the level of transparency of the Takagi-Sugeno neuro-fuzzy model and the support vector machines model by applying them to conflict management, an application which is concerned with causal interpretations of results. The data set used in this investigation is the militarized interstate disputes dataset obtained from the Correlates of War (COW) project. In this chapter, a support vector machine model is trained to predict conflict. Knowledge from the Takagi-Sugeno neuro-fuzzy model is extracted by interpreting the model’s fuzzy rules and their outcomes. It is found that the Takagi-Sugeno neuro-fuzzy model offers some transparency which helps in understanding conflict management. The Takagi-Sugeno neuro-fuzzy model was compared to the support vector machine model and it was found that even though the support vector machine shows marginal advantage over the Takagi-Sugeno neuro-fuzzy model in terms of predictive capacity, the Takagi-Sugeno neuro-fuzzy model allows for linguistics interpretation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abiyev, R.H., Kaynak, O., Alshanableh, T., Mamedov, F.: A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl. Soft Comput. 11, 1396–1406 (2011)

    Article  Google Scholar 

  • Araujo, E.: Improved Takagi-Sugeno fuzzy approach. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1154–1158, Hong Kong (2008)

    Google Scholar 

  • Ata, R., Kocyigit, Y.: An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Syst. Appl. 37, 5454–5460 (2010)

    Article  Google Scholar 

  • Babuska, R.: Fuzzy modeling and identification. Ph.D. thesis, Technical University of Delft, Delft (1991)

    Google Scholar 

  • Babuska, R., Verbruggen, H.: Neuro-fuzzy methods for nonlinear system identification. Annu. Rev. Control 27, 73–85 (2003)

    Article  Google Scholar 

  • Barbieri, K.: Economic interdependence – a path to peace or a source of interstate conflict. J. Peace Res. 33, 29–49 (1996)

    Article  Google Scholar 

  • Beck, N., Katz, J., Tucker, R.: Taking time seriously: time-series cross-section analysis with a binary dependent variable. Am. J. Polit. Sci. 42, 1260–1288 (1998)

    Article  Google Scholar 

  • Beck, N., King, G., Zeng, L.: Improving quantitative studies of international conflict: a conjecture. Am. Polit. Sci. Rev. 94, 21–35 (2000)

    Article  Google Scholar 

  • Biacino, L., Gerla, G.: Fuzzy logic, continuity and effectiveness. Arch. Math. Log. 41, 643–667 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Bih, J.: Paradigm shift – an introduction to fuzzy logic. IEEE Potential. 25(1), 6–21 (2006)

    Article  Google Scholar 

  • Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  • Cabalar, A.F., Cevik, A., Gokceoglu, C., Baykal, G.: Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard Sand mixtures. Expert Syst. Appl. 37, 842–851 (2010)

    Article  Google Scholar 

  • Cano-Izquierdo, J., Almonacid, M., Ibarrola, J.J.: Applying neuro-fuzzy model dFasArt in control systems. Eng. Appl. Artif. Intell. 23, 1053–1063 (2010)

    Article  Google Scholar 

  • Cantor, G.: Über eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen. Crelle. J. F. Math. 77, 258–262 (1874)

    Article  Google Scholar 

  • Cetisli, B.: Development of an adaptive neuro-fuzzy classifier using linguistic hedges: part 1. Expert Syst. Appl. 37, 6093–6101 (2010)

    Article  Google Scholar 

  • Cox, E.: The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, Maintaining Fuzzy Systems. AP Professional, Boston (1994)

    Google Scholar 

  • Demirli, K., Khoshnejad, M.: Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller. Fuzzy Set. Syst. 160, 2876–2891 (2009)

    Article  MathSciNet  Google Scholar 

  • Devlin, K.: The Joy of Sets. Springer, Berlin (1993)

    Book  MATH  Google Scholar 

  • El-Sebakhy, E.A.: Flow regimes identification and liquid-holdup prediction in horizontal multiphase flow based on neuro-fuzzy inference systems. Math. Comp. Simulat. 80, 1854–1866 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  • Ferreirós, J.: Labyrinth of Thought: A History of Set Theory and Its Role in Modern Mathematics. Birkhäuser, Basel (1999)

    MATH  Google Scholar 

  • Habtemariam, E.A., Marwala, T.: Artificial intelligence for conflict management. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 2583–2588, Montreal (2005)

    Google Scholar 

  • Hagiwara, H., Mita, A.: Structural health monitoring system using support vector machine. Adv. Build. Technol. 28, 481–488 (2002)

    Article  Google Scholar 

  • Hájek, P.: Fuzzy logic and arithmetical hierarchy. Fuzzy Set. Syst. 3, 359–363 (1995)

    Article  Google Scholar 

  • Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer, Dordrecht (1998)

    Book  MATH  Google Scholar 

  • Halpern, J.Y.: Reasoning About Uncertainty. MIT Press, Cambridge (2003)

    MATH  Google Scholar 

  • Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  • Hsu, Y.-C., Lin, S.-F.: Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systems. J. Neurocomput. 72, 2418–2432 (2009)

    Article  Google Scholar 

  • Huang, T.M., Kecman, V.: Gene extraction for cancer diagnosis by support vector machines – an improvement. Artif. Intell. Med. 35, 185–194 (2005)

    Article  Google Scholar 

  • Hurtado, J.E.: Relevance of support vector machines for stochastic mechanics. Comput. Fluid. Solid. Mech. 20, 2298–2301 (2003)

    Article  Google Scholar 

  • Iplikci, S.: Support vector machines based neuro-fuzzy control of nonlinear systems. J. Neurocomput. 73, 2097–2107 (2010)

    Article  Google Scholar 

  • Jang, J.-S.R.: ANFIS: Adaptive-network-based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  • Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Toronto (1997)

    Google Scholar 

  • Johnson, P.: A History of Set Theory. Prindle, Weber & Schmidt, Boston (1972)

    MATH  Google Scholar 

  • Jones, D., Bremer, S., Singer, J.: Militarized interstate disputes, 1816–1992 rationale, coding rules and empirical patterns. Conflict Manag. Peace Sci. 15, 585–615 (1996)

    Google Scholar 

  • Khajeh, A., Modarress, H.: Prediction of solubility of gases in polystyrene by adaptive neuro-fuzzy inference system and radial basis function neural network. Expert Syst. Appl 37, 3070–3074 (2010)

    Article  Google Scholar 

  • Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  • Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  • Klir, G.J., St Clair, U.H., Yuan, B.: Fuzzy Set Theory: Foundations and Applications. Prentice Hall, Upper Saddle River (1997)

    MATH  Google Scholar 

  • Kosko, B.: Fuzzy Thinking: The New Science of Fuzzy Logic. Hyperion, New York (1993)

    Google Scholar 

  • Kosko, B., Isaka, S.: Fuzzy logic. Sci. Am. 269, 76–81 (1993)

    Article  Google Scholar 

  • Kucuk, K., Aksoy, C.O., Basarir, H., Onargan, T., Genis, M., Ozacar, V.: Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling. Tunn. Undergr. Sp. Tech. 26, 38–45 (2011)

    Article  Google Scholar 

  • Kurtulus, B., Razack, M.: Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. J. Hydrol. 381, 10–111 (2010)

    Article  Google Scholar 

  • Kwong, C.K., Wong, T.C., Chan, K.Y.: A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Syst. Appl 36, 11262–11270 (2009)

    Article  Google Scholar 

  • Lagazio, M., Russett, B.: A Neural Network Analysis of MIDs, 1885–1992: Are the Patterns Stable? In the Scourge of War: New Extensions on an Old Problem, ch. Towards a Scientific Understanding of War: Studies in Honor of J. David Singer. University of Michigan Press, Ann Arbor (2004)

    Google Scholar 

  • Leke, B., Marwala, T., Tettey, T.: Using inverse neural network for HIV adaptive control. Intl. J. Comput. Intell. Res. 3, 11–15 (2007)

    Google Scholar 

  • Lo, S.: Web service quality control based on text mining using support vector machine. Expert Syst. Appl. 34, 603–610 (2008)

    Article  Google Scholar 

  • Mamdani, E.H.: Application of fuzzy algorithms for the control of a dynamic plant. Proc. IEEE. 121, 1585–1588 (1974)

    Google Scholar 

  • Mansfield, E.D., Snyder, J.: A tale of two democratic peace critiques: a reply to Thompson and Tucker. J. Confl. Res. 41, 457–461 (1997)

    Article  Google Scholar 

  • Marwala, T.: Computational Intelligence for Modelling Complex Systems. Research India Publications, Delhi (2007)

    Google Scholar 

  • Marwala, T.: Computational Intelligence for Missing Data Imputation, Estimation and Management, Knowledge Optimization Techniques. IGI Global Publications, New York (2009)

    Book  Google Scholar 

  • Marwala, T., Lagazio, M.: Modelling and controlling interstate conflict. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1233–1238, Budapest (2004)

    Google Scholar 

  • Marwala, T., Chakraverty, S., Mahola, U.: Fault classification using multi-layer perceptrons and support vector machines. Intl. J. Eng. Simul. 7, 29–35 (2006)

    Google Scholar 

  • Mashrei, M.A., Abdulrazzaq, N., Abdalla, T.Y., Rahman, M.S.: Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Eng. Struct. 32, 1723–1734 (2010)

    Article  Google Scholar 

  • Min, J.H., Lee, Y.-C.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28, 603–614 (2005)

    Article  Google Scholar 

  • Mitra, P., Shankar, B.U., Pal, S.K.: Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recogn. Lett. 25, 1067–1074 (2004)

    Article  Google Scholar 

  • Montazer, G.A., Saremi, H.Q., Khatibi, V.: A neuro-fuzzy inference engine for farsi numeral characters recognition. Expert Syst. Appl. 37, 6327–6337 (2010)

    Article  Google Scholar 

  • Nelwamondo, F.V., Marwala, T., Mahola, U.: Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, mel-frequency cepstral coefficients and fractals. Int. J. Innov. Comput., Info. Control 2, 1281–1299 (2006)

    Google Scholar 

  • Novák, V.: Fuzzy Sets and Their Applications. Adam Hilger, Bristol (1989)

    MATH  Google Scholar 

  • Novák, V.: On fuzzy type theory. Fuzzy Set. Syst. 149, 235–273 (2005)

    Article  MATH  Google Scholar 

  • Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic. Kluwer, Dordrecht (1999)

    Book  MATH  Google Scholar 

  • Oneal, J., Russet, B.: The classical liberals were right: democracy, interdependence and conflict, 1950–1985. Int. Stud. Quart. 41, 267–294 (1997)

    Article  Google Scholar 

  • Oneal, J., Russet, B.: Prediction and classification with neural network models. Sociol. Method. Res. 4, 499–524 (1999)

    Google Scholar 

  • Patel, P.B., Marwala, T.: Forecasting closing price indices using neural networks. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2351–2356, Taipei, Taiwan (2006)

    Google Scholar 

  • Patel, P.B., Marwala, T.: Caller behaviour classification using computational intelligence methods. Int. J. Neural Syst. 20, 87–93 (2010)

    Article  Google Scholar 

  • Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)

    Google Scholar 

  • Sentes, M., Babuska, R., Kaymak, U., van Nauta, L.H.: Similarity measures in fuzzy rule base simplification. IEEE Trans. Syst. Man Cybern. B Cybern. 28, 376–386 (1998)

    Article  Google Scholar 

  • Shiri, J., Kisi, O.: Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J. Hydrol. 394, 486–493 (2010)

    Article  Google Scholar 

  • Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier, Amsterdam (1985)

    Google Scholar 

  • Sugeno, M., Kang, G.: Structure identification of fuzzy model. Fuzzy Set. Syst. 28, 15–33 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  • Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)

    MATH  Google Scholar 

  • Talei, A., Hock, L., Chua, C., Quek, C.: A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling. Expert Syst. Appl. 37, 7456–7468 (2010)

    Article  Google Scholar 

  • Tettey, T.: A computational intelligence approach to modelling interstate conflict: Conflict and causal interpretations. MSc thesis, University of the Witwatersrand, Johannesburg (2007)

    Google Scholar 

  • Tettey, T., Marwala, T.: Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms. In: Proceedings of the 10th International Conference on Intelligent Engineering Systems, pp. 30–34, London (2006a)

    Google Scholar 

  • Tettey, T., Marwala, T.: Neuro-fuzzy modeling and fuzzy rule extraction applied to conflict management. Lect. Note. Comp. Sci. 4234, 1087–1094 (2006b)

    Article  Google Scholar 

  • Tettey, T., Marwala, T.: Conflict modelling and knowledge extraction using computational intelligence methods. In: Proceedings of the 11th IEEE International Conference on Intelligent Engineering Systems, pp. 161–166, Budapest (2007)

    Google Scholar 

  • Thompson, W., Tucker, R.: A tale of two democratic peace critiques. J. Confl. Res 41, 428–454 (1997)

    Article  Google Scholar 

  • Tripathi, S., Srinivas, V.V., Nanjundiah, R.S.: Downscaling of precipitation for climate change scenarios: a support vector machine approach. J. Hydrol. 330, 621–640 (2006)

    Article  Google Scholar 

  • Von Altrock, C.: Fuzzy Logic and NeuroFuzzy Applications Explained. Prentice Hall, Englewood Cliffs (1995)

    Google Scholar 

  • Wright, S., Marwala, T.: Artificial intelligence techniques for steam generator modelling. arXiv:0811.1711 (2006)

  • Zadeh, L.A.: Fuzzy sets. Info. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  • Zemankova-Leech, M.: Fuzzy relational data bases. Ph.D. dissertation, Florida State University, Tallahassee (1983)

    Google Scholar 

  • Zhou, Q., Chan, C.W., Tontiwachwuthikul, P.: An application of neuro-fuzzy technology for analysis of the CO2 capture process. Fuzzy Set. Syst. 161, 2597–2611 (2010)

    Article  Google Scholar 

  • Zimmermann, H.: Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers, Boston (2001)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala & Dr. Monica Lagazio

Authors
  1. Tshilidzi Marwala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Monica Lagazio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tshilidzi Marwala .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Marwala, T., Lagazio, M. (2011). Fuzzy Sets for Modeling Interstate Conflict. In: Militarized Conflict Modeling Using Computational Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-790-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-790-7_6

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-789-1

  • Online ISBN: 978-0-85729-790-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation