Finite Belief Fusion Model for Hidden Source Behavior Change Detection

Eugene Santos Jr., Qi Gu, Eunice E. Santos, John Korah

2012

Abstract

A person’s beliefs and attitudes may change multiple times as they gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. Such influential sources, however, are often invisible to the public due to a variety of reasons – private communications, what one randomly reads or hears, and implicit social hierarchies, to name a few. Many efforts have focused on detecting distribution variations. However, the underlying reason for the variation has yet to be fully studied. In this paper, we present a novel approach and algorithm to detect such hidden sources, as well as capture and characterize the patterns of their impact with regards to the belief-changing trend. We formalize this problem as a finite belief fusion model and solve it via an optimization method. Finally, we compare our work with general mixture models, e.g. Gaussian Mixture Model. We present promising preliminary results obtained from proof-of-concept experiments conducted on both synthetic data and a real-world scenario.

References

  1. Hill, J. L. and Kriesi, H, 2001, An Extension and Test of Converse's "Black-and-White" Model of Response Stability. The American Political Science Review, Vol. 95, No. 2, pp. 397-413
  2. Kelman, HC. 1961. Processes of opinion change. Public Opinion Quarterly. 25:57-78
  3. Garg, A., Jayram T. S., Vaithyanathan. S, Zhu, H, 2004. Generalized Opinion Pooling. In Proceedings of the 8th Intl. Symp. on Artificial Intelligence and Mathematics.
  4. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
  5. Santos, Jr., E., and Santos, E. S. 1999. A framework for building knowledge-bases under uncertainty. Journal. of Experimental and Theoretical Artificial Intelligence 11(2):265-286.
  6. Santos, Jr. E., Wilkinson, J. T, and Santos, E. E, 2011c, Fusing multiple Bayesian knowledge sources. International Journal of Approximate Reasoning, Volume 52, Issue 7, pp 935-947
  7. Das, K., Schneider, J., and Neill, D.B. 2008. Anomaly pattern detection in categorical datasets. Proceeding of the SIGKDD Conf. on Knowledge Discovery and Data Mining, pp 169-176.
  8. García-Teodoro. P, Díaz-Verdejo. J, Maciá-Fernández. G, Vázquez. E. 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers Security, Volume 28, Issues 1-2, pp18-28
  9. Wong, W., Moore, A., Cooper G. and Wagner, M. 2003. Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. Proceedings of the 20th ICML
  10. Dean, T. and Kanazawa, K. 1989. A model for reasoning about persistence and causation, Artificial Intelligence 93(1-2): 1-27
  11. McLachlan G and Peel, D. ?. 2000: Finite mixture models. New York: Wiley
  12. Figueiredo, M. A. T. and Jain, A. K. 2002. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3): 381-396
  13. Gales, M. and Young, S. 2008. The Application of Hidden Markov Models in Speech Recognition, Foundations and Trends in Signal Processing: Vol. 1: No 3, pp 195-304
  14. Robinson, J. W. and Hartemink, A. J. 2010. Learning Non-Stationary Dynamic Bayesian Networks. Journal of Machine Learning. Res. 9999. pp 3647-3680.
  15. Xu, R. and Wunsch, D. 2005. Survey of clustering algorithm. IEEE Transactions on Neural Networks, Vol. 16, No. 3, 645-678
  16. Chan, H and Darwiche, A. 2002. A distance measure for bounding probabilistic belief change. In Proceedings AAAI, pages 539- 545
  17. Santos, E. E.; Santos, E..; Korah, J.; Thompson, J.E.; Keumjoo Kim; George, R.; Gu, Q; Jurmain, J.; Subramanian, S.; Wilkinson, J.T., 2011a, IntentDriven Behavioral Modeling during Cross-Border Epidemics, SocialCom, pp.748-755K.
  18. Santos, Eunice, E., Santos, Eugene, Jr., Wilkinson, John T., Korah, John, Kim, Keumjoo, Li, Deqing, and Yu, Fei, Modeling Complex Social Scenarios using Culturally Infused Social Networks, 2011b, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 3009-3016, Anchorage, AK.
  19. Nocedal, J. and S. J. Wright. Numerical Optimization, 2006, Springer Series in Operations Research, Springer Verlag. Second Edition.
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Paper Citation


in Harvard Style

Santos Jr. E., Gu Q., E. Santos E. and Korah J. (2012). Finite Belief Fusion Model for Hidden Source Behavior Change Detection . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 17-24. DOI: 10.5220/0004130100170024


in Bibtex Style

@conference{kdir12,
author={Eugene Santos Jr. and Qi Gu and Eunice E. Santos and John Korah},
title={Finite Belief Fusion Model for Hidden Source Behavior Change Detection},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={17-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004130100170024},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Finite Belief Fusion Model for Hidden Source Behavior Change Detection
SN - 978-989-8565-29-7
AU - Santos Jr. E.
AU - Gu Q.
AU - E. Santos E.
AU - Korah J.
PY - 2012
SP - 17
EP - 24
DO - 10.5220/0004130100170024