Skip to main content
SpringerLink
Log in

Automatic Relevance Determination for Identifying Interstate Conflict

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

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

  • 569 Accesses

Abstract

This chapter introduces the Bayesian and the evidence frameworks to construct an automatic relevance determination method. These techniques are described in detail, relevant literature reviews were conducted and their use is justified. The automatic relevance determination technique was then applied to determine the relevance of interstate variables that are essential for modeling interstate conflict. Conclusions are drawn and explained within the context of political science.

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

  • Anonymous.: Correlates of war project. http://www.correlatesofwar.org/Last (2010). Accessed 20 Sept 2010

  • Babaie-Kafaki, S., Ghanbari, R., Mahdavi-Amiri, N.: Two new conjugate gradient methods based on modified secant equations. J. Comput. Appl. Math. 234, 1374–1386 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  • Barton, D.N., Saloranta, T., Moe, S.J., Eggestad, H.O., Kuikka, S.: Bayesian belief networks as a meta-modelling tool in integrated river basin management – pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian River Basin. Ecol. Econ. 66, 91–104 (2008)

    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 

  • Bernardo, J.M.: Reference posterior distributions for Bayesian inference. J. R. Stat. Soc. 41, 113–147 (1979)

    MathSciNet  MATH  Google Scholar 

  • Bernardo, J.M.: Reference analysis. Handb. Stat. 25, 17–90 (2005)

    Article  MathSciNet  Google Scholar 

  • Bertsekas, D.P.: Non-linear Programming. Athenas Scientific, Cambridge (1995)

    Google Scholar 

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

    Google Scholar 

  • Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis. Wiley, Hoboken (1973)

    MATH  Google Scholar 

  • Browne, A.: Using neural networks with automatic relevance determination to identify regions of the thalamus implicated in Schizophrenia. In: Proceedings of the IEEE International Joint Conference on Neural Networks. pp. 97–101, Vancouver (2006)

    Google Scholar 

  • Bryson, A.E., Ho, Y.C.: Applied Optimal Control: Optimization, Estimation, and Control. Xerox College Publishing, Kentucky (1989)

    Google Scholar 

  • Chiddarwar, S.S., Babu, N.R.: Comparison of RBF and MLP neural networks to solve inverse kinematic problems for 6R serial robots by a fusion approach. Eng. Appl. Artif. Intel 23, 1083–1092 (2010)

    Article  Google Scholar 

  • Cybenko, G.: Approximations by superpositions of sigmoidal functions. Math.Control. Signal. Syst 2, 303–314 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  • Ebrahimzadeh, A., Khazaee, A.: Detection of premature ventricular contractions using mlp neural networks: a comparative study. Measurement 43, 103–112 (2010)

    Article  Google Scholar 

  • Edwards, A.W.F.: Likelihood. Cambridge University Press, Cambridge (1972)

    MATH  Google Scholar 

  • Fienberg, S.E.: When did Bayesian inference become “Bayesian”? Bayesian. Anal 1, 1–40 (2006)

    Article  MathSciNet  Google Scholar 

  • Fletcher, R.: Practical Methods of Optimization. Wiley, New York (1987)

    MATH  Google Scholar 

  • Freeman, J., Skapura, D.: Neural Networks: Algorithms, Applications and Programming Techniques. Addison-Wesley, Reading (1991)

    MATH  Google Scholar 

  • Fu, Y., Browne, A.: Using ensembles of neural networks to improve automatic relevance determination. In: Proceeding of the IEEE International Joint Conference on Neural Networks, pp. 1590–1594, Orlando (2007)

    Google Scholar 

  • Gelpi, C., Griesdorf, M.: Winners and losers? Democracies in international crisis, 1918–94. Am. Polit. Sci. Rev 95, 633–647 (2001)

    Article  Google Scholar 

  • Ghate, V.N., Dudul, S.V.: Optimal MLP neural network classifier for fault detection of three phase induction motor. Expert Syst. Appl. 37, 3468–3481 (2010)

    Article  Google Scholar 

  • Gleditsch, K.S., Wards, M.D.: Peace and war in time and space: the role of democratization. Int. Stud. Q. 43, 1–29 (2000)

    Article  Google Scholar 

  • Gochman, C., Maoz, Z.: Militarized interstate disputes 1816–1976. In: Singer, D., Diehl, P. (eds.) Measuring the Correlates of War. University of Michigan Press, Ann Arbor (1990)

    Google Scholar 

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

    Google Scholar 

  • Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge (1995)

    MATH  Google Scholar 

  • Haykin, S.: Neural Networks. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  • Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. J. Res. Nat. Bur. Stand 6, 409–436 (1952)

    MathSciNet  Google Scholar 

  • Jaynes, E.T.: Prior probabilities. IEEE Trans. Syst. Sci. Cyb. 4, 227–241 (1968)

    Article  Google Scholar 

  • Kelly, D.L., Smith, C.L.: Bayesian inference in probabilistic risk assessment – the current state of the art. Reliab. Eng. Syst. Saf. 94, 628–643 (2009)

    Article  Google Scholar 

  • Kim, G., Kim, Y., Lim, H., Kim, H.: An MLP-based feature subset selection for HIV-1 protease cleavage site analysis. Artif. Intell. Med. 48, 83–89 (2010)

    Article  Google Scholar 

  • Lagazio, M., Marwala, T.: Assessing different bayesian neural network models for militarized interstate dispute. Soc. Sci. Comput. Rev. 24, 1–12 (2005)

    Google Scholar 

  • Lagazio, M., Russet, B.: A neural network analysis of MIDs, 1885–1992: are the patterns stable? In: Diehl, P. (ed.) Toward a Scientific Understanding of War: Studies in Honor of J. David Singer. University of Michigan Press, Ann Arbor (2002)

    Google Scholar 

  • Lazkano, E., Sierra, B., Astigarraga, A., Martínez-Otzeta, J.M.: On the use of Bayesian networks to develop behaviours for mobile robots. Robot. Auton. Syst. 55, 253–265 (2007)

    Article  Google Scholar 

  • Lee, P.M.: Bayesian Statistics, an Introduction. Wiley, Hoboken (2004)

    MATH  Google Scholar 

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

    Google Scholar 

  • Lisboa, P.J.G., Etchells, T.A., Jarman, I.H., Arsene, C.T.C., Aung, M.S.H., Eleuteri, A., Taktak, A.F.G., Ambrogi, F., Boracchi, P., Biganzoli, E.: Partial logistic artificial neural network for competing risks regularized with automatic relevance determination. IEEE Trans. Neural. Nets 20, 1403–1416 (2009)

    Article  Google Scholar 

  • Luenberger, D.G.: Linear and Non-linear Programming. Addison-Wesley, Reading (1984)

    Google Scholar 

  • Lunga, D., Marwala, T.: On-line forecasting of stock market movement direction using the improved incremental algorithm. Lecture Notes in Computer Science, vol. 4234, pp. 440–449, Springer Heidelberg (2006)

    Google Scholar 

  • MacKay, D.J.C.: Bayesian methods for adaptive models. PhD thesis, California Institute of Technology (1991)

    Google Scholar 

  • MacKay, D.J.C.: A practical Bayesian framework for back propagation networks. Neural Comput. 4, 448–472 (1992)

    Article  Google Scholar 

  • Marwala, T.: On damage identification using a committee of neural networks. J. Eng. Mech. 126, 43–50 (2000)

    Article  Google Scholar 

  • Marwala, T.: Probabilistic fault identification using a committee of neural networks and vibration data. J. Aircraft 38, 138–146 (2001)

    Article  Google Scholar 

  • Marwala, T.: Fault classification using pseudo modal energies and neural networks. Am. Inst. Aeronaut. Astronaut. J. 41, 82–89 (2003)

    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.: Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Springer, Heidelberg (2010)

    Book  MATH  Google Scholar 

  • Marwala, T., Hunt, H.E.M.: Fault identification using finite element models and neural networks. Mech. Syst. Signal. Process 13, 475–490 (1999)

    Article  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., Sibisi, S.: Finite element model updating using bayesian framework and modal properties. J. Aircraft 42, 275–278 (2005)

    Article  Google Scholar 

  • Mohamed, N., Rubin, D., Marwala, T.: Detection of epileptiform activity in human EEG signals using bayesian neural networks. Neural Info. Process Lett. Rev. 10, 1–10 (2006)

    Google Scholar 

  • Møller, A.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Nets 6, 525–533 (1993)

    Article  Google Scholar 

  • Mordecai, A.: Non-linear Programming: Analysis and Methods. Dover Publishing, New York (2003)

    Google Scholar 

  • Neal, R.M.: Bayesian learning for neural networks. PhD thesis, University of Toronto (1994)

    Google Scholar 

  • Neal, R.M.: Assessing the relevance determination methods using DELVE. In: Bishop, C.M. (ed.) Neural Nets and Machine Learn. Springer, Berlin (1998)

    Google Scholar 

  • Nummenmaa, A., Auranen, T., Hämäläinen, M.S., Jääskeläinen, I.P., Sams, M., Vehtari, A., Lampinen, J.: Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. NeuroImage 37, 876–889 (2007)

    Article  Google Scholar 

  • Oneal, J.R., Russett, B.: Clear and clean: the fixed effects of democracy and economic interdependence. Int. Organ 3, 469–486 (2001)

    Article  Google Scholar 

  • Patel, P., Marwala, T.: Neural networks, fuzzy inference systems and adaptive-neuro fuzzy inference systems for financial decision making. Lecture Notes in Computer Science, vol. 4234, pp. 430–439, Springer Heidelberg (2006)

    Google Scholar 

  • Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  • Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan, Washington, DC (1961)

    Google Scholar 

  • Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)

    Google Scholar 

  • Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  • Sahin, F., Yavuz, M.Ç., Arnavut, Z., Uluyol, Ö.: Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Comput. 33, 124–143 (2007)

    Article  MathSciNet  Google Scholar 

  • Smyrnakis, M.G., Evans, D.J.: Classifying ischemic events using a Bayesian inference multilayer perceptron and input variable evaluation using automatic relevance determination. Comput. Cardiol. 34, 305–308 (2007)

    Article  Google Scholar 

  • Stigler, S.M.: The History of Statistics. Harvard University Press, Cambridge (1986)

    MATH  Google Scholar 

  • Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  • Ulusoy, I., Bishop, C.M.: Automatic relevance determination for the estimation of relevant features for object recognition. In: Proceedings of the IEEE 14th Signal Processing and Communication Applications, pp. 1–4, Antalya (2006)

    Google Scholar 

  • Uusitalo, L.: Advantages and challenges of Bayesian networks in environmental modelling. Ecol. Model. 203, 312–318 (2007)

    Article  Google Scholar 

  • Van Calster, B., Timmerman, D., Nabney, I.T., Valentin, L., Van Holsbeke, C., Van Huffel, S.: Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: a multi-center study. In: Proceedings of the Engineering in Medicine and Biology Society, pp. 5342–5345, New York (2006)

    Google Scholar 

  • Vilakazi, B.C., Marwala, T.: Condition monitoring using computational intelligence. In: Laha, D., Mandal, P. (eds.) Handbook on Computational Intelligence in Manufacturing and Production Management, illustrated edn. IGI Publishers, New York (2007)

    Google Scholar 

  • Wang, D., Lu, W.Z.: Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination. Model. Chemosphere 62, 1600–1611 (2006)

    Google Scholar 

  • Werbos, P.J.: Beyond regression: new tool for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University (1974)

    Google Scholar 

  • Wu, W., Chen, Z., Gao, S., Brown, E.N.: Hierarchical Bayesian modeling of inter-trial variability and variational Bayesian learning of common spatial patterns from multichannel EEG. In: Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing, pp. 501–504, Montreal (2010)

    Google Scholar 

  • Yoon, Y., Peterson, L.L.: Artificial neural networks: An emerging new technique. In: Proceedings of the ACM SIGBDP Conference on Trends and Directions in Expert Systems, pp. 417–422, Orlando (1990)

    Google Scholar 

  • Zeng, L.: Prediction and classification with neural network models. Soc. Method Res. 27, 499–524 (1999)

    Article  Google Scholar 

  • Zhang, J., Liu, S., Wang, Y.: Gene association study with SVM, MLP, and cross-validation for the diagnosis of diseases. Prog. Nat. Sci. 18, 741–750 (2008)

    Article  MathSciNet  Google Scholar 

  • Zhao, Z., Xin, H., Ren, Y., Guo, X.: Application and comparison of BP neural network algorithm in MATLAB. In: Proceedings of the International Conference on Measurement Technology and Mechatron Automat, pp. 590–593, New York (2010)

    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). Automatic Relevance Determination for Identifying 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_2

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-790-7_2

  • 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