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A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm

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Abstract

Bayesian networks (BNs) are widely used as one of the most effective models in bioinformatics, artificial intelligence, text analysis, medical diagnosis, etc. Learning the structure of BNs from data can be viewed as an optimization problem and is proved that this problem is NP-hard. Therefore, heuristic methods can be used as powerful tools to find high-quality networks. In this paper, an interesting approach which is based on Breeding Swarm has been used to learn BNs. Breeding Swarm is a hybrid GA/PSO which enable us to benefits the strengths of particle swarm optimization with genetic algorithms. In order to assess the proposed method, several real-world and benchmark applications are used. Results show that our method is a clear improvement on genetic algorithm and particle swarm optimization.

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References

  • Abramson B et al (1996) Hailfinder: a Bayesian system for forecasting severe weather. Int J Forecast 12:57–71

    Article  Google Scholar 

  • Ahmad FK, Deris S, Othman N (2012) The inference of breast cancer metastasis through gene regulatory networks. J Biomed Inform 45:350–362

    Article  Google Scholar 

  • Alonso-Barba JI, Puerta JM (2011) Structural learning of Bayesian networks using local algorithms based on the space of orderings. Soft Comput 15:1881–1895

    Article  MATH  Google Scholar 

  • BayesiaLab 6.0.2, Bayesia SAS, Laval, France. http://www.bayesialab.com

  • Beinlich IA (1989) The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. Springer, Berlin

    Google Scholar 

  • Binder J et al (1997) Adaptive probabilistic networks with hidden variables. Mach Learn 29:213–244

    Article  MATH  Google Scholar 

  • Cheng J, Bell DA and Liu W (1997) Learning belief networks from data: an information theory based approach. In: Proceedings of the sixth international conference on Information and knowledge management. ACM, pp 325–331

  • Chickering DM, Geiger D, Heckerman D (1995) Learning bayesian networks: search methods and experimental results. In: 5th international workshop on artificial intelligence and statistics, pp 112–128

  • Chickering DM (1996) Learning Bayesian networks is NP-complete. In: Fisher D, Lenz H-J (eds) Learning from data, vol 112. Springer, New York, pp 121–130

  • Chickering DM (2002) Optimal structure identification with greedy search. J Mach Learn Res 3:507–554

    MathSciNet  MATH  Google Scholar 

  • Colace F, De Santo M, Vento M (2010) A multiexpert approach for Bayesian network structural learning. In: 43rd Hawaii international conference on system sciences (HICSS), 2010 . IEEE pp 1–11

  • Colombo D, Maathuis MH (2014) Order-independent constraint-based causal structure learning. J Mach Learn Res 15:3741–3782

    MathSciNet  MATH  Google Scholar 

  • Cowell R (1998) Introduction to inference for Bayesian networks. In: Jordan MI (ed) Learning in graphical models, vol 89. Springer, Netherlands, pp 9–26

  • Cowie J, Oteniya L, Coles R (2007) Particle swarm optimisation for learning Bayesian networks. World Congress on Engineering, Newswood Limited/International Association of Engineers (IAENG), pp 71–76

  • Da You L et al (2001) Research on learning bayesian network structure based on genetic algorithms. J Comput Res Dev 8:916–922 (in Chinese)

    Google Scholar 

  • Friedman N, Nachman I, Peér D (1999) Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 206–215

  • Gámez JA, Mateo JL, Puerta JM (2011) Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood. Data Min Knowl Discov 22:106–148

    Article  MathSciNet  MATH  Google Scholar 

  • Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20:197–243

    MATH  Google Scholar 

  • Hill SM et al (2012) Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28:2804–2810

    Article  Google Scholar 

  • Jensen AL, Jensen FV (1996) MIDAS-an influence diagram for management of mildew in winter wheat. In: Proceedings of the twelfth international conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 349–356

  • Ji J et al (2011) A hybrid method for learning Bayesian networks based on ant colony optimization. Appl Soft Comput 11:3373–3384

    Article  Google Scholar 

  • Ji J, Wei H, Liu C (2013) An artificial bee colony algorithm for learning Bayesian networks. Soft Comput 17:983–994

    Article  Google Scholar 

  • Khanteymoori AR, Menhaj MB, Homayounpour MM (2011) Structure learning in Bayesian networks using asexual reproduction optimization. ETRI J 33:39–49

    Article  Google Scholar 

  • Larrañaga P et al (2013) A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf Sci 233:109–125

    Article  MathSciNet  MATH  Google Scholar 

  • Larrañaga P et al (1996) Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Trans Pattern Anal Mach Intell 18:912–926

    Article  Google Scholar 

  • Lauritzen SL, Spiegelhalter DJ (1988) Local computations with probabilities on graphical structures and their application to expert systems. J R Stat Soc B 50:157–224

    MathSciNet  MATH  Google Scholar 

  • Li Z et al (2011) Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics 27:2686–2691

    Article  Google Scholar 

  • Mattew S, Terence S (2006) Breeding PSO: a GA/PSO Hybrid. Department of Computer Science, University of Idaho, Moscow

  • Munteanu P, Bendou M (2001) The EQ framework for learning equivalence classes of Bayesian networks. In: Proceedings ieee international conference on data mining, 2001. ICDM 2001. IEEE, pp 417–424

  • Murphy K, Mian S (1999) Modelling gene expression data using dynamic Bayesian networks. Technical report, Computer Science Division, University of California, Berkeley, CA

  • NorsysSoftwareCorp, 1990–2013. Netica. Version 5.12. http://www.norsys.com/

  • Robinson RW (1977) Counting unlabeled acyclic digraphs. In: Little CHC (eds) Combinatorial mathematics V. Lecture Notes in Mathematics, vol 622. Springer, Heidelberg, pp 28–43

  • Scheines R et al (1998) The TETRAD project: constraint based aids to causal model specification. Multivar Behav Res 33:65–117

    Article  Google Scholar 

  • Spiegelhalter DJ, Cowell RG (1992) Learning in probabilistic expert systems. Bayesian Stat 4:447–465

    MathSciNet  Google Scholar 

  • Spirtes P, Glymour CN, Scheines R (2000) Causation, prediction, and search. MIT press, Cambridge

    MATH  Google Scholar 

  • Statnikov A (2010) Causal explorer: a matlab library of algorithms for causal discovery and variable selection for classification. Causation Predict Chall Chall Mach Learn 2:267

    Google Scholar 

  • Tsamardinos I, Brown LE, Aliferis CF (2006) The max–min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65:31–78

    Article  Google Scholar 

  • Wong ML, Lam W, Leung KS (1999) Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. In: IEEE transactions on pattern analysis and machine intelligence, pp 174–178

  • Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf Sci 180:1942–1954

    Article  Google Scholar 

  • Ziegler V (2008) Approximation algorithms for restricted Bayesian network structures. Inf Process Lett 108:60–63

    Article  MathSciNet  MATH  Google Scholar 

  • Zou M, Conzen SD (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21:71–79

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Engineering, University of Zanjan, Zanjan, Iran

    Ali Reza Khanteymoori, Mohammad-H. Olyaee & Omid Abbaszadeh

  2. Islamic Azad University, Karaj Branch, Karaj, Iran

    Maryam Valian

Authors
  1. Ali Reza Khanteymoori
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  2. Mohammad-H. Olyaee
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  3. Omid Abbaszadeh
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  4. Maryam Valian
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Correspondence to Ali Reza Khanteymoori.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Khanteymoori, A.R., Olyaee, MH., Abbaszadeh, O. et al. A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm. Soft Comput 22, 3049–3060 (2018). https://doi.org/10.1007/s00500-017-2557-z

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