Skip to main content
SpringerLink
Log in

Fitness Function Based on Binding and Recall Rate for Genetic Inductive Logic Programming

  • Conference paper
Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7331))

Included in the following conference series:

  • 3183 Accesses

Abstract

The key of using genetic inductive logic programming (GILP) algorithm to learn first-order rules is how to precisely evaluate the quality of first-order rules. That is, the fitness of rules should rightly score their quality and effectively guide GILP algorithm to be close to the target rule. In this paper, a new fitness function is proposed. By adopting the concept of binding, the new fitness function can adequately utilize the information hidden in background knowledge and training examples. By considering recall rate of rules, the new fitness function can avoid generating over-specific rules. Experiments on benchmark data set show that comparing with the common fitness function based on amount of examples covered by rules, the new fitness function can measure quality of first-order rules more precisely and enhance predictive accuracy of GILP.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Li, Y., Guo, M.: Web page classification using relational learning algorithm and unlabeled data. Journal of Computers 6(3), 474–479 (2011)

    Google Scholar 

  2. Fröhler, S., Kramer, S.: Inductive logic programming for gene regulation prediction. Machine Learning 70(2), 225–240 (2008)

    Article  Google Scholar 

  3. Biba, M., Xhafa, F., Esposito, F., Ferilli, S.: Stochastic simulation and modeling of metabolic networks in a machine learning framework. Simulation Modeling Practice and Theory 19, 1957–1966 (2011)

    Article  Google Scholar 

  4. Dehbi, Y., Plmer, L.: Learning grammar rules of building parts from precise models and noisy observations. Journal of Photogrammetry and Remote Sensing 66, 166–176 (2011)

    Article  Google Scholar 

  5. Kavurucu, Y., Senkul, P., Toroslu, I.H.: A comparative study on ILP-based concept discovery systems. Expert Systems with Applications 38, 11598–11607 (2011)

    Article  Google Scholar 

  6. De Raedt, L.: Logical and relational learning. Springer (2008)

    Google Scholar 

  7. Kavurucu, Y., Senkul, P., Toroslu, I.H.: Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement. Knowledge-Based Systems 23, 747–756 (2010)

    Article  Google Scholar 

  8. Mutlu, A., Senkul, P., Kavurucu, Y.: Improving the scalability of ILP-based multi-relational concept discovery system through parallelization. Knowledge-Based Systems 27, 352–368 (2012)

    Article  Google Scholar 

  9. Quinlan, J.R.: Learning Logical Definitions from Relations. Machine Learning 5(3), 239–266 (1990)

    Google Scholar 

  10. Raedt, L.D., Dehaspe, L.: Clausal discovery. Machine Learning 26, 99–146 (1997)

    Article  MATH  Google Scholar 

  11. Jiang, H., Yang, X., Yin, K., Zhang, S., Cristoforo, J.A.: Multi-path QoS-Aware Web Service Composition using Variable Length Chromosome Genetic Algorithm. Information Technology Journal 10, 113–119 (2011)

    Article  Google Scholar 

  12. Giordana, A., Sale, C.: Learning structured concepts using genetic algorithms. In: Sleeman, D., Edwards, P. (eds.) The 9th International Workshop on Machine Learning. Morgan Kaufmann (1992)

    Google Scholar 

  13. Muggleton, S., Tamaddoni-Nezhad, A.: QG/GA: a stochastic search for Progol. Machine Learning 70, 121–133 (2008)

    Article  Google Scholar 

  14. Chien, Y.-W.C., Chen, Y.-L.: A phenotypic genetic algorithm for inductive logic programming. Expert Systems with Applications 36, 6935–6944 (2009)

    Article  Google Scholar 

  15. Chien, Y.-W.C., Chen, Y.-L.: Mining associative classification rules with stock trading data-A GA-based method. Knowledge-Based Systems 23, 605–614 (2010)

    Article  Google Scholar 

  16. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5(3), 239–266 (1990)

    Google Scholar 

  17. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning (2006)

    Google Scholar 

  18. Li, Y., Guo, M.: A relational learning algorithm combining relational tri-training and relational instance-based learning. Journal of Information and Computational Science 9(2), 425–436 (2012)

    MathSciNet  Google Scholar 

  19. Yang, X., Liu, C., Zhang, J.: Using genetic algorithm to mine first-order rules. Computer Engineering and Applications 38(17), 28–30 (2002) (in Chinese)

    Google Scholar 

  20. Yang, X., Liu, C.: Grouwh phenomenon of individuals’ code length in genetic inductive logic programming. Journal of Computer Research and Development 8, 1238–1243 (2003) (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yanjuan Li & Maozu Guo

  2. School of Information and Computer Engineering, North-East Forestry University, Harbin, China

    Yanjuan Li

Authors
  1. Yanjuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maozu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, Y., Guo, M. (2012). Fitness Function Based on Binding and Recall Rate for Genetic Inductive Logic Programming. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30976-2_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30975-5

  • Online ISBN: 978-3-642-30976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation