Binary Ant Colony Optimization for Subset Problems | SpringerLink
Skip to main content
Springer Nature Link
Log in

Binary Ant Colony Optimization for Subset Problems

  • Chapter
  • First Online:
Multi-objective Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 592))

Abstract

Many optimization problems involve selecting the best subset of solution components. Besides, many other optimization problems can be modelled as a subset problem. This chapter focuses on developing a new framework in ant colony optimization (ACO) for optimization problems that require selection rather than ordering with an application to feature selection for regression problems as a representative for subset problems. This is addressed through three steps that are: explaining the main guidelines of developing an ant algorithm, demonstrating different solution representations for subset problems using ACO algorithms, and proposing a binary ant algorithm for feature selection for regression problems.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
JPY 14299
Price includes VAT (Japan)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Montgomery, E.J.: Solution biases and pheromone representation selection in ant colony optimization. Ph.D Thesis, Bond University, Australia (2005)

    Google Scholar 

  2. Solnon, C., Bridge, D.: An ant colony optimization meta-heuristic for subset selection problems. In: Nedjah, N., Mourelle, L.M. (eds.) Systems Engineering Using Particle Swarm Optimization, pp. 3–25. Nova Science Publishers, New York (2006)

    Google Scholar 

  3. Mirzayans, T., Parimi, N., Pilarski, P., Backhouse, C., Wyard-Scott, L., Musilek, P.: A swarm-based system for object recognition. Neural Netw. World 15, 243–255 (2005)

    Google Scholar 

  4. Piatrik, T., Chandramouli, K., Izquierdo, E.: Image classification using biologically inspired systems. In: Proceedings of the 2nd International Mobile Multimedia Communications Conference MobiMedia’06, pp. 18–20 (2006)

    Google Scholar 

  5. Dorigo, M., Bonabeou, E., Theraulaz, G.: Inspiration for optimization from social insect behavior. Nature 406, 39–42 (2000)

    Article  Google Scholar 

  6. Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  7. Maniezzo, V., Milandri, M.: An ant-based framework for very strongly constrained problems. In: Dorigo, M., et al. (eds.) Ants Algorithms. LNCS, pp. 222–227. Springer, Berlin (2002)

    Chapter  Google Scholar 

  8. Cordon, O., Herrera, F., Stutzle, T.: A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathw. Soft Comput. 9(3), 141–175 (2002)

    MATH  MathSciNet  Google Scholar 

  9. Galea, M.: Applying swarm intelligence to rule induction. M.Sc. Thesis, Division of Informatics, University of Edinburgh (2002)

    Google Scholar 

  10. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  11. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston (1998)

    Book  MATH  Google Scholar 

  12. Klosgen, W., Zytkow, J.M.: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)

    Google Scholar 

  13. Cios, K.J., Pedrycz, W., Wiswiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers, Boston (1998)

    Book  MATH  Google Scholar 

  14. Weiss, S., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers, San Francisco (1998)

    MATH  Google Scholar 

  15. Rokach, L., Maimon, O.: Data Mining with Decision Trees. World Scientific Publishing, Singapore (2008)

    MATH  Google Scholar 

  16. Gong, S., McKenna, S., Psarrou, A.: Dynamic Vision: From Images to Face Recognition. Imperial College Press, London (1999)

    Google Scholar 

  17. Jahne, B., Haussecker, H., Geissler, P.: Handbook of Computer Vision and Applications. Academic Press, San Diego (1999)

    Google Scholar 

  18. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Chapman & Hall Computing, New York (1993)

    Book  Google Scholar 

  19. Bradski, G., Kaehler, A.: Learning OpenCV. O’Reilly, California (2008)

    Google Scholar 

  20. Jain, A., Flynn, P., Ross, A.: Handbook of Biometrics. Springer, New York (2008)

    Book  Google Scholar 

  21. Whelan, P., Molloy, D.: Machine Vision Algorithms in Java: Techniques and Implementation. Springer, New York (2001)

    Book  Google Scholar 

  22. Le, D., Satoh, S.: An efficient feature selection method for object detection. Pattern Recognition and Data Mining. LNCS, pp. 461–468. Springer, Berlin (2005)

    Chapter  Google Scholar 

  23. Serre, T., Heisele, B., Mukherjee, S., Poggio, T.: Feature Selection for Face Detection. Massachusetts Institute of Technology, Cambridge (2000)

    Google Scholar 

  24. Lesk, A.: Introduction to Bioinformatics. Oxford University Press, New York (2002)

    Google Scholar 

  25. Silva, P., Hashimoto, R., Kim, S., Barrera, J.: Feature selection algorithms to find strong genes. Pattern Recognit. Lett. 26, 1444–1453 (2005)

    Article  Google Scholar 

  26. Baxevanis, A., Quellette, B.F.F.: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. Wiley, New York (2001)

    Book  Google Scholar 

  27. Riboni, D.: Feature selection for web page classification. In: Proceedings of the Workshop EURASIA-ICT 2002, pp. 473–477 (2002)

    Google Scholar 

  28. Sun, A., Lim, E., Ng, W.: Web classification using support vector machine. In: Proceedings of the WIDM’02, November, McLean, Virginia (2002)

    Google Scholar 

  29. Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning (ICML 1997), pp. 412–420 (1997)

    Google Scholar 

  30. Chen, J., Huang, H., Tian, S., Qua, Y.: Feature selection for text classification with naive bayes. Expert Syst. Appl. 36, 5432–5435 (2009)

    Article  Google Scholar 

  31. Erta, F.: Feature selection and classification techniques for speaker recognition. J. Eng. Sci. 7(1), 47–54 (2001)

    Google Scholar 

  32. Ye, N.: The Handbook of Data Mining. Lawrence Erlbaum Associates, Mahwah (2003)

    Google Scholar 

  33. Abd-Alsabour, N., Randall, M., Lewis, A.: Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. In: Proceedings of the PDCAT, China (2012)

    Google Scholar 

  34. Han, J., Kamber, M.: Data Mining Concepts and Techniques, 1st edn. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  35. Skillicorn, D.: Understanding Datasets: Datamining with Matrix Decomposition. Chapman & Hall/CRC, London (2007)

    Book  Google Scholar 

  36. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. Massachusetts Institute of Technology, Cambridge (2001)

    Google Scholar 

  37. Berry, M., Linoff, G.: Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd edn. Wiley, New York (2004)

    Google Scholar 

  38. Leguizam’on, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of Congress on Evolutionary Computation (CEC99), Washington DC, July 6–9. IEEE Press, (1999)

    Google Scholar 

  39. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization- artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 28–39 (2006)

    Google Scholar 

  40. Lee, K., Joo, J., Yang, J., Honavar, V.: Experimental comparison of feature subset selection using GA and ACO algorithm. In: Li, X., Zaiane, O.R., Li, Z. (eds.) ADMA 2006. LNAI, pp. 465–472. Springer, Berlin (2006)

    Google Scholar 

  41. Bello, R., Nowe, A., Caballero, Y., Gomez, Y., Vrancx, P.: A model based on ant colony system and rough set theory to feature selection. In: Proceedings of the GECCO’05. Washington (2005)

    Google Scholar 

  42. Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the Workshop on Computational Intelligence, UK. 15–22 (2003)

    Google Scholar 

  43. Aghdam, M., Tanha, J., Naghsh-Nilchi, A., Basiri, M.: Combination of ant colony optimization and Bayesian classification for feature selection in a bioinformatics dataset. J. Comput. Sci. Syst. Biol. 2(3), 186–199 (2009)

    Article  Google Scholar 

  44. Trafalis, T.B., Ince, H.: Support vector machine for regression and applications to financial forecasting. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference, vol. 6, pp. 348–353. IEEE Press (2000)

    Google Scholar 

  45. Durbha, S.S., King, R.L., Younan, N.H.: Support vector machines regression for retrieval of leaf area index from multi-angle imaging spectroradiometer. Remote Sens. Environ. 107, 348–361 (2007)

    Article  Google Scholar 

  46. Lui, B.: Web Data Mining. Springer, Berlin (2010)

    Google Scholar 

  47. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, Taipei, Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf, (2003), viewed 25th March 2013

  48. Wang, L., Fu, X.: Data Mining with Computational Intelligence. Springer, Berlin (2005)

    MATH  Google Scholar 

  49. Marsland, S.: Machine Learning: An Algorithmic Perspective, CRC Press-Taylor & Francis Group, (2009)

    Google Scholar 

  50. Miller, T.: Data and Text Mining- A Business Applications Approach. Pearson/ Prentice Hall, Upper Saddle River (2005)

    Google Scholar 

  51. Novat, A.: On the role of feature selection in machine learning. Ph.D Thesis, Hebrew University, Israel (2006)

    Google Scholar 

  52. http://www.cs.waikato.ac.nz/ml, viewed 25th March 2013

  53. Amasyali, M., Ersoy, O.: A comparative review of regression ensembles on drug design datasets. Turkish J. Electr. Eng. Comput. Sci. 1–17 (2013)

    Google Scholar 

  54. R: A Language and Environment for Statistical Computing 2006 [http://www.R-project.org]. R Foundation for Statistical Computing, Vienna, Austria

  55. http://www.cs.waikato.ac.nz/ml/weka, viewed 25th March 2011

  56. Shen, Q., Jiang, J.H., Tao, J.C., Shen, G.L., Yu, R.Q.: Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. J. Chem. Inf. Model. 45, 1024–1029 (2005)

    Article  Google Scholar 

  57. Izrailev, S., Agrafiotis, D.: Variable selection for QSAR by artificial ant colony systems. SAR QSAR Environ. Res. 13, 417–423 (2002)

    Article  Google Scholar 

  58. Gunturi, S., Narayanan, R., Khandelwal, A.: In silico ADME modeling 2: computational models to predict human serum albumin binding affinity using ant colony systems. Biorgan. Med. Chem. 14, 4118–4129 (2006)

    Article  Google Scholar 

  59. Shamsipur, M., Zare-Shahabadi, V., Hemmateenejad, B., Akhond, M.: Ant colony optimization: a powerful tool for wavelength selection. J. Chemom. 20, 146–157 (2006)

    Article  Google Scholar 

  60. Palanisamy, S., Kanmani, S.: Artificial bee colony approach for optimizing feature selection. Int. J. Comput. Sci. Issues 9(3), 432–438 (2012)

    Google Scholar 

  61. Shamsipur, M., Zare-Shahabadi, V., Hemmateenejad, B., Akhond, M.: An efficient variable selection method based on the use of external memory in ant colony optimization: application to QSAR/QSPR. Anal. Chim. Acta 646, 39–46 (2009)

    Article  Google Scholar 

  62. Vieira, S.M., Sousa, J.M.C., Runkler, T.A.: Two cooperative ant colonies for feature selection using fuzzy models. Expert Syst. Appl. 37, 2714–2723 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cairo University Cairo, Cairo, Egypt

    Nadia Abd-Alsabour

Authors
  1. Nadia Abd-Alsabour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadia Abd-Alsabour .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  2. Department of Computer Science and Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, India

    Alok Kumar Jagadev

  3. Gandhi Institute for Technological Advancement, Bhubaneswar, India

    Mrutyunjaya Panda

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Abd-Alsabour, N. (2015). Binary Ant Colony Optimization for Subset Problems. In: Dehuri, S., Jagadev, A., Panda, M. (eds) Multi-objective Swarm Intelligence. Studies in Computational Intelligence, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46309-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46309-3_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46308-6

  • Online ISBN: 978-3-662-46309-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation