Map-reduce Implementation of Belief Combination Rules

Frédéric Dambreville

2016

Abstract

This paper presents a generic and versatile approach for implementing combining rules on preprocessed belief functions, issuing from a large population of information sources. In this paper, we address two issues, which are the intrinsic complexity of the rules processing, and the possible large amount of requested combinations. We present a fully distributed approach, based on a map-reduce (Spark) implementation.

References

  1. Dambreville, F. (2009). Definition of evidence fusion rules based on referee functions, volume 3. American Research Press.
  2. Dean, J. and Ghemawat, S. (2008). Mapreduce: Simplified data processing on large clusters.Commun. ACM, 51(1):107-113.
  3. Dempster, A. P. (1968). A generalization of bayesian inference. J. Roy. Statist. Soc., B(30):205-247.
  4. Dubois, D. and Prade, H. (1986). On the unicity of dempster rule of combination. International Journal of Intelligent Systems, 1(2):133-142.
  5. Florea, M., Dezert, J., Valin, P., Smarandache, F., and Jousselme, A. (2006). Adaptative combination rule and proportional conflict redistribution rule for information fusion. In COGnitive systems with Interactive Sensors, Paris, France.
  6. Lefevre, E., Colot, O., and Vannoorenberghe, P. (2002). Belief functions combination and conflict management. Information Fusion Journal, 3(2):149-162.
  7. Liu, W., Miller, P., Ma, J., and Yan, W. (2009). Challenges of distributed intelligent surveillance system with heterogenous information. In Workshop on Quantitative Risk Analysis for Security Applications, Pasadena, California.
  8. Martin, A. and Osswald, C. (2007). Toward a combination rule to deal with partial conflict and specificity in belief functions theory. In International Conference on Information Fusion, Qébec, Canada.
  9. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.
  10. Smarandache, F. and Dezert, J. (2005). Information fusion based on new proportional conflict redistribution rules. In International Conference on Information Fusion, Philadelphia, USA.
  11. Smets, P. (1990). The combination of evidences in the transferable belief model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5):447-458.
  12. Zaharia, M., Chowdhury, M., Franklin, M., Shenker, S., and Stoica, I. (2010). Spark: Cluster computing with working sets. In Proceedings of 2nd USENIX Conference on Hot Topics in Cloud Computing, Berkeley, CA USA.
  13. Zhou, K., Martin, A., and Pan, Q. (2015a). A similaritybased community detection method with multiple prototype representation. Physica A: Statistical Mechanics and its Applications, 438:519-531.
  14. Zhou, K., Martin, A., Pan, Q., and Liu, Z. (2015b). Median evidential c-means algorithm and its application to community detection. Knowledge-Based Systems, 74:69-88.
  15. m1(Y1)m2(Y2)
Download


Paper Citation


in Harvard Style

Dambreville F. (2016). Map-reduce Implementation of Belief Combination Rules . In Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-193-9, pages 144-149. DOI: 10.5220/0005987001440149


in Bibtex Style

@conference{data16,
author={Frédéric Dambreville},
title={Map-reduce Implementation of Belief Combination Rules},
booktitle={Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2016},
pages={144-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005987001440149},
isbn={978-989-758-193-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Map-reduce Implementation of Belief Combination Rules
SN - 978-989-758-193-9
AU - Dambreville F.
PY - 2016
SP - 144
EP - 149
DO - 10.5220/0005987001440149