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Words, antibodies and their interactions

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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.

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References

  • 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 

  • 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 

  • 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.

    MATH  Google Scholar 

  • Cayzer, S., & Aickelin, U. (2002). A recommender system based on the immune network. Tech. Rep. HPL-2002-1, HP Laboratories Bristol, UK.

  • Coutinho, A. (1995). The network theory: 21 years later (editorial). Scandinavian Journal of Immunology, 42(1), 3–8.

    Article  MathSciNet  Google Scholar 

  • De Boer, R. J. (1991). Recent developments in idiotypic network theory. Netherlands Journal of Medicine, 39, 254–262.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • de Castro, L. N., & Timmis, J. (2002). Artificial immune systems: a new computational intelligence approach. Heidelberg: Springer.

    MATH  Google Scholar 

  • 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 

  • Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462. http://www.scholarpedia.org/article/Swarm_intelligence.

    Article  Google Scholar 

  • Farmer, J. D., Packard, N. H., & Perelson, A. S. (1986). The immune system, adaptation, and machine learning. Physica D, 22, 187–204.

    Article  MathSciNet  Google Scholar 

  • Fehr, T., & Caspar, C. B. (2001). Idiotype network. In Encyclopedia of life sciences. New York: Wiley.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • Jerne, N. K. (1973). Towards a network theory of the immune system. Annals of Immunology, 125(C), 373–389.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • McElligott, M., & Sorensen, H. (1994). An evolutionary connectionist approach to personal information filtering. In 4th Irish neural networks conference ’94 (pp. 141–146).

  • McEwan, C., & Hart, E. (2009). Representation in the (artificial) immune system. Journal of Mathematical Modelling and Algorithms, 8(2), 125–149.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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 

  • Nanas, N., & De Roeck, A. (2009a). Autopoiesis the immune system and adaptive information filtering. Natural Computing, 8(2), 387–427.

    Article  MathSciNet  Google Scholar 

  • 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).

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • Nanas, N., Vavalis, M., & Houstis, E. (2010). Personalised news and scientific literature aggregation. Information Processing and Management, 46, 268–283.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.

    Google Scholar 

  • 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.

  • Rocchio, J. (1971). Relevance feedback in information retrieval (pp. 313–323). Upper Saddle River: Prentice-Hall. Chap. 14.

    Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Varela, F. J., & Coutinho, A. (1991). Second generation immune network. Immunology Today, 12(5), 159–166.

    Google Scholar 

  • Vaz, N. M., & Varela, F. (1978). Self and non-sense: An organism-centered approach to immunology. Medical Hypotheses, 4, 231–267.

    Article  Google Scholar 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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 

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Authors and Affiliations

  1. Centre for Research and Technology—Thessaly, Lab for Information Systems and Services, CE.RE.TE.TH, Volos, Greece

    Nikolaos Nanas

  2. Department of Computer & Communication Engineering, University of Thessaly, Volos, Greece

    Manolis Vavalis

  3. Computing and Mathematics Department, The Open University, Milton Keynes, UK

    Anne De Roeck

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  1. Nikolaos Nanas
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  2. Manolis Vavalis
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  3. Anne De Roeck
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Correspondence to Nikolaos Nanas.

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Nanas, N., Vavalis, M. & De Roeck, A. Words, antibodies and their interactions. Swarm Intell 4, 275–300 (2010). https://doi.org/10.1007/s11721-010-0044-6

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  • DOI: https://doi.org/10.1007/s11721-010-0044-6

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