Skip to main content
SpringerLink
Log in

Parallel Genetic Algorithm Approach to Automated Discovery of Hierarchical Production Rules

  • Conference paper
Applications of Soft Computing

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 58))

Abstract

It is important to discover hierarchical decision rules from databases because much of the world’s knowledge is best expressed in the form of hierarchies. Mining of decision rules at multiple concept levels leads to discovery of more informative and comprehensible knowledge. This paper proposes automated discovery of Hierarchical Production Rules (HPR) using a parallel genetic algorithm approach. A combination of degree of subsumption and coefficient of similarity has been used as a quantitative measure of hierarchical relationship among the classes. An island/deme GA is designed to evolve HPRs for the classes of the dataset being mined. The island model exploits control as well as data parallelism. The model is applied to a synthetic dataset on means of transport and results are presented.

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aguilar-Ruiz, J.S., Requelme, J.C., Toro, M.: Evolutionary Learning of Hierarchical Decision Rules. IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetic. 33, 324–331 (2003)

    Article  Google Scholar 

  2. Araujo, D.L.A., Lopes, H.S., Freitas, A.A.: A Parallel Genetic Algorithm for Rule Discovery in Large Databases. In: Proc. IEEE Systems, Man and Cybernetics Conference, Tokyo, vol. 3, pp. 940–945 (1999)

    Google Scholar 

  3. Bharadwaj, K.K., Al-Maqaleh, B.M.: Evolutionary Approach for Automated Discovery of Censored Production Rules. Enformatika 10, 147–152 (2005)

    Google Scholar 

  4. Bharadwaj, K.K., Al-Maqaleh, B.M.: Evolutionary Approach for Automated Discovery of Censored Production Rules with Fuzzy Hierarchy. In: International Multi-conference of Engineers and Computer Scientists, Hong kong, pp. 716–721 (2007)

    Google Scholar 

  5. Bharadwaj, K.K., Jain, N.K.: Hierarchical Censored Production Rules (HCPRs) System. Data Mining and Knowledge Engg. 8, 19–34 (1992)

    Article  Google Scholar 

  6. Bharadwaj, K.K., Jain, N.K.: Some Learning Techniques in Hierarchical Censored Production Rules (HCPRs) System. International Journal of Intelligent Systems 13, 319–344 (1997)

    Google Scholar 

  7. Centu-Paz, E.: A Survey of Parallel Genetic Algorithm. Calculateurs Paralleles, Reseaux et Systems, Repatis. 10, 141–147 (1998)

    Google Scholar 

  8. Enrique, A., Tomassini, M.: Parallel and Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 6, 443–462 (2002)

    Article  Google Scholar 

  9. Fayyad, U.M., Shapiro, G.P., Smyth, P.: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communication of ACM 39, 27–34 (1996)

    Article  Google Scholar 

  10. Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Berlin (2002)

    MATH  Google Scholar 

  11. Kandwal, R., Bharadwaj, K.K.: Cumulative Growth of Production Rules with Fuzzy Hierarchy (PRFH). In: Proceeding of the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC  2006), Palma De Mallorca, Spain, pp. 40–45. ACTA Press (2006)

    Google Scholar 

  12. Michalski, R.S., Winston, P.H.: Variable Precision Logic. Artificial Intelligence 29, 121–146 (1986)

    Article  MATH  Google Scholar 

  13. Saroj, S., Bharadwaj, K.K.: A Parallel Genetic Algorithm Approach to Automated Discovery of Censored Production Rules. In: Proceedings of IASTED International Conference on Artificial Intelligence and Applications (AIA 2007), Innsbruck, Austria, pp. 435–441. ACTA press (2007)

    Google Scholar 

  14. Tsumoto, S.: Automated Extraction of Hierarchical Decision Rules from Clinical Databases using Rough Set Model. Expert System Applications 24, 189–197 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    K. K. Bharadwaj

  2. Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, 125001, Haryana, India

    Saroj

Authors
  1. K. K. Bharadwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saroj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Applied Science (SAS) Decision Engineering Centre, Cranfield University , MK43 0AL, Cranfield, Bedfordshire, UK

    Jörn Mehnen  & Ashutosh Tiwari  & 

  2. Dept. of Artificial Intelligence Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology Kawazu , 820-8502, Iizuka, Fukuoka, Japan

    Mario Köppen

  3. Computer Science Department School of Computing, Armstrong Atlantic State University , 31419, Savannah, GA, USA

    Ashraf Saad

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bharadwaj, K.K., Saroj (2009). Parallel Genetic Algorithm Approach to Automated Discovery of Hierarchical Production Rules. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 58. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89619-7_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89619-7_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89618-0

  • Online ISBN: 978-3-540-89619-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation