Skip to main content
Log in

A social intelligent system for multi-objective optimization of classification rules using cultural algorithms

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using “IF-THEN” rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Baykasoglu A, Ozbakir L (2007) MEPAR-miner: multi-expression programming for classification rule mining. Eur J Oper Res 183:767–784

    Article  MATH  Google Scholar 

  2. Berlanga F, Del Jesus MJ, Gonzalez P, Herrera F, Mesonero M (2006) Multi-objective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing. In: Perner P (ed) ICDM 2006 LNAI 4065. Springer, Berlin, pp 337–349

    Google Scholar 

  3. Casillas J, Orriols-Puig A, Bernad_o-Mansilla E (2008) Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems. In: proceedings of 3rd international workshop on genetic and evolving fuzzy systems. Witten-Bommerholz, Germany, pp 89–94

  4. Casillas J, Pedro Martinez AE, Benitez Alicia D (2009) Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems. Soft Computing 13:419–465

    Article  Google Scholar 

  5. Cao L (2009) Introduction to agent mining interaction and integration. In: Cao L (ed) Data mining and multi-agent integration LLC 2009. Springer, Berlin, pp 3–36

    Chapter  Google Scholar 

  6. Dehuri S, Mall R (2006) Predictive and comprehensible rule discovery using a multi-objective genetic algorithm. Knowledge-Based Syst 19:413–421

    Article  Google Scholar 

  7. De la Iglesia B, Philpott MS, Bagnall AJ, Rayward-Smith VJ (2003) Data mining rules using multi-objective evolutionary algorithms. In: proceedings of 2003 IEEE congress on, evolutionary computation, pp 1552–1559

  8. De la Iglesia B, Reynolds Alan, Rayward-Smith Vic J (2005) Developments on a multi-objective meta-heuristic (MOMH) algorithm for finding interesting sets of classification rules. In: Proceedings of third international conference on evolutionary multi-criterion optimization, EMO2005, LNCS 3410. Springer, Berlin, pp 826–840

  9. Del Jesus MJ, G Pedro, H Francisco (2007) Multi-objective genetic algorithm for extracting subgroup discovery fuzzy rules. In: Proceedings of the IEEE symposium on computational intelligence in multi-criteria decision making, pp 50–57

  10. Freitas AA (2007) A review of evolutionary algorithms for data mining. In: Soft computing for knowledge discovery and data mining. Springer, USA, pp 79–111

  11. Giusti Rafael, Gustavo EA, Batista PA, Prati Ronaldo Cristiano (2008) Evaluating ranking composition methods for multi-objective optimization of knowledge rules. In: Proceedings of eighth international conference on hybrid intelligent systems, pp 537–542

  12. Ishibuchi H, Murata T, Turksen IB (1997) Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 89(2):135–150

    Article  Google Scholar 

  13. Ishibuchi H, Nakashima T, Murata T (2001) Three-objective genetics-based machine learning for linguistic rule extraction. Inf Sci 136(1–4):109–133

    Article  MATH  Google Scholar 

  14. Ishibuchi H, Namba S (2004) Evolutionary multi-objective knowledge extraction for high-dimensional pattern classification problems. Parallel problem solving from nature–PPSN VIII, LNCS 3242. Springer, Berlin, pp 1123–1132

  15. Ishibuchi H, Nojima Y (2005) Comparison between fuzzy and interval partitions in evolutionary multi-objective design of rule-based classification systems. In: Proceedings of the 2005 IEEE international onference on fuzzy systems, pp 430–435

  16. Ishibuchi H (2007) Evolutionary multi-objective design of fuzzy rule-based systems. In: Proceedings of the 2007 IEEE symposium on foundations of computational intelligence (FOCI 2007), pp 9–16

  17. Ishibuchi H, Kuwajima I, Nojima Y (2007) Multi-objective classification rule mining, natural computing series. Springer, Berlin, pp 219–240

  18. Kendall Graham, Yan Su (2007) Imperfect evolutionary systems. IEEE Trans Evolut Comput 11(3):294–307

    Article  Google Scholar 

  19. Khabzaoui M, Dhaenens C, Talbi EG (2008) Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery. RAIRO Oper Res 42:69–83

    Article  MathSciNet  MATH  Google Scholar 

  20. Lazar Alina, Reynolds RG (2002) Heuristic. In: Ruhul A Sarker, Hussein A Abbass, Charles S Newton (eds) Heuristics and optimization for knowledge discovery, vol 2. Idea Group Publishing, USA

    Google Scholar 

  21. Narukawa K, Nojima Y, Ishibuchi H (2005) Modification of evolutionary multi-objective optimization algorithms for multi-objective design of fuzzy rule-based classification systems. In: Proceedings of the 2005 IEEE international conference on fuzzy systems, pp 809–814

  22. Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases. Department of Information and Computer Science, University of California at Irvine, http://http://archive.ics.uci.edu/ml

  23. Reynolds AP, de la Iglesia B (2006) Rule induction using multi-objective meta-heuristic: Encouraging rule diversity. In: Proceedings of IJCNN 2006, pp 6375–6382

  24. Reynolds AP, de la Iglesia B (2007) Rule Induction for classification using multi-objective genetic programming. In: Proceedings of 4th international conference on evolutionary multi-criterion optimization. LNCS 4403:516–530

  25. Reynolds AP, de la Iglesia B (2009) A multi-objective GRASP for partial classification. Soft Comput 13(3):227–243

    Article  Google Scholar 

  26. Reynolds AP, Corne David W, De la Iglesia B (2009) A multi-objective grasp for rule selection. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, GECCO’09, Montréal Québec, Canada, pp 643–650

  27. Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the 3rd annual conference on evolutionary programming. World Scientific, River Edge, NJ, pp 131–139

  28. Reynolds RG, Peng Bin, Ali Mostafa (2007) The role of culture in the emergence of decision-making roles, an example using cultural algorithms. Complexity Wiley Periodicals Inc 13(3):27–42

    Google Scholar 

  29. Reynolds RG, Ali M, Jayyousi T (2008) Mining the social fabric of archaic urban centers with cultural algorithms. IEEE Comput 41:64–72

    Article  Google Scholar 

  30. Sujatha S, Ramakrishnan S (2011) Evolutionary multi-objective optimization for rule mining: a review. Artif Intell Rev 36(3):205–248. doi:10.1007/s10462-011-9212-3

    Article  Google Scholar 

  31. Sternberg M, Reynolds RG (1997) Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic environments: a case study in fraud detection. IEEE Trans Evol Comput 1(4):225–243

    Article  Google Scholar 

  32. Wang H, Kwong S, Jin Y, Wei W, Man KF (2005) Agent based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Trans Syst Man Cybern 35(2):143–155

    Article  Google Scholar 

  33. Whitacre JM (2011) Recent trends indicate rapid growth of nature-inspired optimization in academia and industry. Computing 93:121–133. doi:10.1007/s00607-011-0154-z

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sujatha Srinivasan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Srinivasan, S., Ramakrishnan, S. A social intelligent system for multi-objective optimization of classification rules using cultural algorithms. Computing 95, 327–350 (2013). https://doi.org/10.1007/s00607-012-0246-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-012-0246-4

Keywords

Mathematics Subject Classification (2000)

Navigation