Immunology as a Metaphor for Computational ... - Napier University

Chapter 6. A Self-Organising SDM — SOSDM 177a method for selecting training examples during evolution of a classification functiontreeusing Genetic Programming. Cases are selected on the premise that it is of benefitto focus attention on those cases which are currently difficult, i.e. are currently misclassified,and also on those cases which have not been looked at for several iterations.Thus cases are selected for training with a bias that is based on both the difficulty and’age’ of the case. However, both this method and FPS suffer from the drawback thatthey effectively implement a type of supervised learning, whereas ideally an immunesystem based model would be truly unsupervised. Furthermore, they both require therecall accuracy of each item to be calculated following every iteration — this is notalways desirable and adds a considerable time overhead, especially if the data-set islarge.Experiments performed in dynamic environments showed that SOSDM rapidlyadapts to data that is changing within fixed clusters, being able to return to its previouslevels of accuracy within a few iterations. It responds less well to the appearanceof entirely new clusters, though as previously noted, this is an unlikely scenario in areal-world situation. Certainly new clusters will appear over the course of time, howeverit is likely to be a slow and gradual process. Furthermore, the quality of theresponse is somewhat dependent on the characteristics of the dataset. One radical solutionwould be to periodically restart the algorithm from scratch using the currentdata; this is feasible given the short time-scales required to run the algorithm, howeverit has the major disadvantage that all historical information contained in the countersis lost. New methods of dealing with the formation of new clusters will be tackled inthe future, and are likely to be closely related to the mechanisms incorporated in thealgorithm for adding and deleting antibodies dynamically.Currently the system incorporates simple mechanisms for determining when todelete and add antibodies. The addition mechanism is fairly crude in that it detectsstagnation of the system simply by monitoring movement of the antibodies, and thenadds a new antibody in a random position. This could be made more sophisticatedby adding the new antibody in a part of the input space which is not well representedby the current system, for example by interpolating between the antibodies with thelargest accumulated error, as in Fritzke’s GNG algorithm. The criterion for determin-