Advertisement

Swarm Intelligence

, Volume 4, Issue 4, pp 275–300 | Cite as

Words, antibodies and their interactions

  • Nikolaos NanasEmail author
  • Manolis Vavalis
  • Anne De Roeck
Article

Abstract

Jerne’s idiotypic network theory stresses the importance of antibody-to-antibody interactions and provides possible explanations for self-tolerance and increased diversity in the immune repertoire. In this paper, we use an immune network model to build a user profile for adaptive information filtering. Antibody-to-antibody interactions in the profile’s network model correlations between words in text. The user profile has to be able to represent a user’s multiple interests and adapt to changes in them over time. This is a complex and dynamic engineering problem with clear analogies to the immune process of self-assertion. We present a series of experiments investigating the effect of term correlations on the user’s profile performance. The results show that term correlations can encode additional information, which has a positive effect on the profile’s ability to assess the relevance of documents to the user’s interests and to adapt to changes in them.

Keywords

Immune network Autopoiesis Information filtering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bersini, H. (2002). Self-assertion versus self-recognition: A tribute to Francisco Varela. In J. Timmis & P. J. Bentley (Eds.), ICARIS 2002: 1st international conference on artificial immune systems (pp. 107–112). Canterbury: University of Kent at Canterbury Printing Unit. Google Scholar
  2. Bezerra, G. B., Barra, T. V., Ferreira, H. M., Knidel, H., de Castro, L. N., & Zuben, F. J. V. (2006). An immunological filter for spam. In H. Bersini & J. Carneiro (Eds.), Lecture notes in computer science : Vol. 4163. Artificial immune systems, 5th international conference (ICARIS 2006) (pp. 446–458). Heidelberg: Springer. Google Scholar
  3. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press, Santa Fe Institute Studies in the Sciences of Complexity. zbMATHGoogle Scholar
  4. Cayzer, S., & Aickelin, U. (2002). A recommender system based on the immune network. Tech. Rep. HPL-2002-1, HP Laboratories Bristol, UK. Google Scholar
  5. Coutinho, A. (1995). The network theory: 21 years later (editorial). Scandinavian Journal of Immunology, 42(1), 3–8. CrossRefMathSciNetGoogle Scholar
  6. De Boer, R. J. (1991). Recent developments in idiotypic network theory. Netherlands Journal of Medicine, 39, 254–262. Google Scholar
  7. De Boer, R. J., & Perelson, A. S. (1991). Size and connectivity as emergent properties of a developing immune network. Journal of Theoretical Biology, 149, 381–424. CrossRefGoogle Scholar
  8. de Castro, L. N., & Timmis, J. (2002). Artificial immune systems: a new computational intelligence approach. Heidelberg: Springer. zbMATHGoogle Scholar
  9. de Castro, L. N., & Zuben, F. J. V. (2001). aiNet: An artificial immune network for data analysis. In H. A. Abbass, R. A. Sarker, & C. S. Newton (Eds.), Data mining: a heuristic approach (pp. 231–259). Hershey: Idea Group Publishing. Google Scholar
  10. Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462. http://www.scholarpedia.org/article/Swarm_intelligence. CrossRefGoogle Scholar
  11. Farmer, J. D., Packard, N. H., & Perelson, A. S. (1986). The immune system, adaptation, and machine learning. Physica D, 22, 187–204. CrossRefMathSciNetGoogle Scholar
  12. Fehr, T., & Caspar, C. B. (2001). Idiotype network. In Encyclopedia of life sciences. New York: Wiley. Google Scholar
  13. Garrett, S. M. (2003). A paratope is not and epitope: Implications for immune network models and clonal selection. In J. Timmis, P. Bentley, & E. Hart (Eds.), Lecture notes in computer science : Vol. 2787. Artificial immune systems, second international conference (ICARIS 2003) (pp. 217–228). Heidelberg: Springer. Google Scholar
  14. Hart, E., Bersini, H., & Santos, F. (2006). Tolerance vs intolerance: How affinity defines topology in an idiotypic network. In H. Bersini & J. Carneiro (Eds.), Lecture notes in computer science : Vol. 4163. Artificial immune systems, 5th international conference (ICARIS 2006) (pp. 109–121). Heidelberg: Springer. Google Scholar
  15. Hart, E., Santos, F., & Bersini, H. (2007). Topological constraints in the evolution of idiotypic networks. In L. N. de Castro, F. J. V. Zuben, & H. Knidel (Eds.), Lecture notes in computer science : Vol. 4628. Artificial immune systems, 6th international conference (ICARIS 2007) (pp. 252–263). Heidelberg: Springer. Google Scholar
  16. Hershberg, U., Solomon, S., & Cohen, I. R. (2003). What is the basis of the immune system’s specificity? In V. Capasso (Ed.), Mathematical modelling & computing in biology and medicine, The MIRIAM project series, Progetto Leonardo (pp. 377–384). Bologna: ESCULAPIO Pub. Co. Google Scholar
  17. Jerne, N. K. (1973). Towards a network theory of the immune system. Annals of Immunology, 125(C), 373–389. Google Scholar
  18. Kassab, R., & Lamirel, J. C. (2006). An innovative approach to intelligent information filtering. In SAC ’06: proceedings of the 2006 ACM symposium on applied computing (pp. 1089–1093). New York: ACM Press. CrossRefGoogle Scholar
  19. Maturana, H. R., & Varela, F. J. (1980). Boston studies in the philosophy of science : Vol. 42. Autopoiesis and cognition: the realization of the living. Dordrecht: Reidel. Google Scholar
  20. McElligott, M., & Sorensen, H. (1994). An evolutionary connectionist approach to personal information filtering. In 4th Irish neural networks conference ’94 (pp. 141–146). Google Scholar
  21. McEwan, C., & Hart, E. (2009). Representation in the (artificial) immune system. Journal of Mathematical Modelling and Algorithms, 8(2), 125–149. zbMATHCrossRefMathSciNetGoogle Scholar
  22. Mendao, M., Timmis, J., Andrews, P. S., & Davies, M. (2007). The immune system in pieces: Computational lessons from degeneracy in the immune system. In IEEE symposium on foundations of computational intelligence (FOCI 2007) (pp. 394–400). Piscataway: IEEE Press. CrossRefGoogle Scholar
  23. Morrison, T., & Aickelin, U. (2002). An artificial immune system as a recommender for web sites. In ICARIS 2002: 1st international conference on artificial immune systems (pp. 161–169). Canterbury: University of Kent at Canterbury Printing Unit. Google Scholar
  24. Nanas, N., & De Roeck, A. (2007). Multimodal dynamic optimisation: from evolutionary algorithms to artificial immune systems. In Lecture notes in computer science : Vol. 4628. Artificial immune systems, 6th international conference (ICARIS 2007) (pp. 13–24). Heidelberg: Springer. Google Scholar
  25. Nanas, N., & De Roeck, A. (2009a). Autopoiesis the immune system and adaptive information filtering. Natural Computing, 8(2), 387–427. CrossRefMathSciNetGoogle Scholar
  26. Nanas, N., & De Roeck, A. (2009b). A review of evolutionary and immune inspired information filtering. Natural Computing. http://www.springerlink.com/content/g523m8328856gpn4/ (online first).
  27. Nanas, N., & Vavalis, M. (2008). A “bag” or a “window” of words for information filtering. In J. Darzentas, G. A. Vouros, S. Vosinakis, & A. Arnellos (Eds.), Lecture notes in computer science : Vol. 5138. Artificial intelligence: theories, models and applications, 5th Hellenic conference on AI (SETN 2008) (pp. 182–193). Heidelberg: Springer. CrossRefGoogle Scholar
  28. Nanas, N., Uren, V., De Roeck, A., & Domingue, J. (2003). Building and applying a concept hierarchy representation of a user profile. In 26th annual international ACM SIGIR conference on research and development in information retrieval (pp. 198–204). New York: ACM Press. Google Scholar
  29. Nanas, N., Uren, V., & De Roeck, A. (2004a). Nootropia: a user profiling model based on a self-organising term network. In G. Nicosia, V. Cutello, P. J. Bentley, & J. Timmis (Eds.), Lecture notes in computer science : Vol. 3239. Artificial immune systems, third international conference (ICARIS 2004) (pp. 146–160). Heidelberg: Springer. Google Scholar
  30. Nanas, N., Uren, V., De Roeck, A., & Domingue, J. (2004b). Multi-topic information filtering with a single user profile. In Lecture notes in computer science : Vol. 3025. Methods and applications of artificial intelligence, third Hellenic conference on AI (SETN 2004) (pp. 400–409). Heidelberg: Springer. Google Scholar
  31. Nanas, N., Uren, V., & Roeck, A. D. (2004c). A comparative evaluation of term weighting methods in information filtering. In 4th international workshop on natural language and information systems (NLIS ’04) (pp. 13–17). Washington: IEEE Computer Society Press. Google Scholar
  32. Nanas, N., Vavalis, M., & De Roeck, A. (2009a). What happened to content based information filtering? In L. Azzopadi, G. Kazai, S. Robertson, S. Ruger, M. Shokouhi, D. Song, & E. Yilmaz (Eds.), Lecture notes in computer science : Vol. 5766. Advances in information retrieval theory, second international conference on the theory of information retrieval (ICTIR 2009) (pp. 249–256). Heidelberg: Springer. Google Scholar
  33. Nanas, N., Vavalis, M., & Kellis, L. (2009b). Immune learning in a dynamic information environment. In Lecture notes in computer science : Vol. 5666. Artificial immune systems, 8th international conference (ICARIS 2009) (pp. 192–205). Heidelberg: Springer. Google Scholar
  34. Nanas, N., Vavalis, M., & Houstis, E. (2010). Personalised news and scientific literature aggregation. Information Processing and Management, 46, 268–283. CrossRefGoogle Scholar
  35. Neal, M. (2003). Meta-stable memory in an artificial immune network. In J. Timmis, P. J. Bentley, & E. Hart (Eds.), Lecture notes in computer science : Vol. 2787. Artificial immune systems, second international conference (ICARIS 2003) (pp. 168–180). Heidelberg: Springer. Google Scholar
  36. Oda, T., & White, T. (2005). Immunity from spam: An analysis of an artificial immune system for junk email detection. In Lecture notes in computer science : Vol. 3627. Artificial immune systems, 4th international conference (ICARIS 2005) (pp. 276–289). Heidelberg: Springer. Google Scholar
  37. Perelson, A., & Oster, G. (1979). Theoretical studies of clonal selection: Minimal antibody repertoire size and reliability of self-non-self discrimination. Journal of Theoretical Biology, 81, 645–670. CrossRefMathSciNetGoogle Scholar
  38. Pon, R. K., Cárdenas, A. F., & Buttler, D. J. (2008). Online selection of parameters in the Rocchio algorithm for identifying interesting news articles. In WIDM ’08: proceeding of the 10th ACM workshop on web information and data management (pp. 141–148). New York: ACM Press. CrossRefGoogle Scholar
  39. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137. Google Scholar
  40. Robertson, S., & Soboroff, I. (2001). The TREC 2001 filtering track report. In The tenth text retrieval conference (TREC-10) (pp. 26–37). NIST Special Publication 500-250. Google Scholar
  41. Rocchio, J. (1971). Relevance feedback in information retrieval (pp. 313–323). Upper Saddle River: Prentice-Hall. Chap. 14. Google Scholar
  42. Secker, A., Freitas, A. A., & Timmis, J. (2003). AISEC: an artificial immune system for e-mail classification. In R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D. Essam, & T. Gedeon (Eds.), Congress on evolutionary computation (pp. 131–139). Piscataway: IEEE Press. Google Scholar
  43. Sorensen, H., O’ Riordan, A., & O’ Riordan, C. (1997). Profiling with the informer text filtering agent. Journal of Universal Computer Science, 3(8), 988–1006. Google Scholar
  44. Stewart, J., & Varela, F. J. (1991). Morphogenesis in shape-space, elementary meta-dynamics in a model of the immune network. Journal of Theoretical Biology, 153, 477–498. CrossRefGoogle Scholar
  45. Varela, F. J., & Coutinho, A. (1991). Second generation immune network. Immunology Today, 12(5), 159–166. Google Scholar
  46. Vaz, N. M., & Varela, F. (1978). Self and non-sense: An organism-centered approach to immunology. Medical Hypotheses, 4, 231–267. CrossRefGoogle Scholar
  47. Veltkamp, R. C., Burkhardt, H., & Kriegel, H. P. (Eds.) (2008). State-of-the-art in content-based image and video retrieval. New York: Barnes & Noble. Google Scholar
  48. Yang, Y., Yoo, S., Zhang, J., & Kisiel, B. (2005). Robustness of adaptive filtering methods in a cross-benchmark evaluation. In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval (pp. 98–105). New York: ACM Press. CrossRefGoogle Scholar
  49. Zhang, Y. (2004). Using Bayesian priors to combine classifiers for adaptive filtering. In SIGIR ’04: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval (pp. 345–352). New York: ACM Press. Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2010

Authors and Affiliations

  • Nikolaos Nanas
    • 1
    Email author
  • Manolis Vavalis
    • 2
  • Anne De Roeck
    • 3
  1. 1.Centre for Research and Technology—ThessalyLab for Information Systems and Services, CE.RE.TE.THVolosGreece
  2. 2.Department of Computer & Communication EngineeringUniversity of ThessalyVolosGreece
  3. 3.Computing and Mathematics DepartmentThe Open UniversityMilton KeynesUK

Personalised recommendations