We have developed a bio-inspired solution for binary classification of textual documents inspired by T-cell cross-regulation in the vertebrate adaptive immune system, which is a complex adaptive system of millions of cells interacting to distinguish between self and nonself substances. In analogy, automatic document classification assumes that the interaction and co-occurrence of thousands of words in text can be used to identify conceptually-related classes of documents—at a minimum, two classes with relevant and irrelevant documents for a given concept (e.g. articles with protein-protein interaction information). Our agent-based method for document classification expands the analytical model of Carneiro et al, by allowing us to deal simultaneously with many distinct populations of antigen-specific T-Cells and their collective dynamics. We have extended this model to produce a spam-detection system. We have also developed our agent-based model further to apply it to biomedical article classification, testing it on a dataset of biomedical articles provided by the BioCreative 2.5 challenge. Our results are useful for biomedical text mining, but they also help us understand T-cell cross-regulation as a potential general principle of classification available to collectives of molecules without a central controller. While there is still much to know about the specifics of T-cell cross-regulation in adaptive immunity, Artificial Life allows us to explore alternative emergent classification principles while producing useful bio-inspired tools. Recently, we started expanding this algorithm to other forms of classification such as sensor data from human-robot interactions under an IUCRG project.
Project partially funded by:
- Indiana University Collaborative Research Grants 2013. Project title: “Social SLAM: Creating Dynamical Socio-Environmental Models for Mobile Robots”.
- IARPA Contract: Early Model-Based Event Recognition with Surrogates (EMBERS), 2012-2014.
Selected Project Publications
- A. Abi-Haidar . “An adaptive document classifier inspired by T-Cell cross-regulation in the immune system” (pdf). PhD Dissertation, Indiana University
- A. Abi-Haidar and L.M. Rocha . “Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross-Regulation Dynamics“. Evolutionary Intelligence. 4(2):69-80. DOI: 10.1007/s12065-011-0052-5.
- A. Abi-Haidar and L.M. Rocha . “Collective Classification of Biomedical Articles using T-Cell Cross-regulation“. In: Artificial Life XII: Twelfth International Conference on the Simulation and Synthesis of Living Systems. H. Fellermann et al et al (Eds.). MIT Press, pp. 706-713.
- A. Abi-Haidar and L.M. Rocha . “Biomedical Article Classification Using an Agent-Based Model of T-Cell Cross-Regulation“. In: Artificial Immune Systems: 9th International Conference, (ICARIS 2010). E. Hart, C. McEwan, J. Timmis, and A. Hone (Eds.) Lecture Notes in Computer Science. Springer-Verlag, 6209: 237-249. Recipient of Best Paper Award. for ICARIS 2010
- A. Abi-Haidar and L.M. Rocha . Adaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift“. In: Artificial Immune Systems: 7th International Conference, (ICARIS 2008). Bentley, Peter; Lee, Doheon; Jung, Sungwon (Eds.) Lecture Notes in Computer Science. Springer-Verlag, 5132: 36-47.
- A. Abi-Haidar and L.M. Rocha . Adaptive Spam Detection Inspired by the Immune System“. In: Artificial Life XI: Eleventh International Conference on the Simulation and Synthesis of Living Systems. S. Bullock, J. Noble, R. A. Watson, and M. A. Bedau (Eds.). MIT Press, pp. 1-8.