1. Applicability Issues of the (Real-valued) Negative Selection Algorithms Zhou Ji, Dipankar Dasgupta The University of Memphis GECCO 2006: July 11, 2006. Seattle.

2. outline Background Background Applicable or not? Applicable or not? Whether and when Whether and when Issue by issue Issue by issue Conclusion Conclusion

3. Background Artificial Intelligence … Biology-inspired methods … Neural network Evolutionary computation Artificial immune system (AIS) … Immune network Clonal selection Negative selection algorithms Other models

4. Basic idea of negative selection algorithms (NSA): The problem to solve: anomaly detection or one-class classification

5. Basic idea of negative selection algorithms (NSA): Possible detectors are generated randomly.

6. Basic idea of negative selection algorithms (NSA): Those that cover self region are eliminated.

7. Variety of NSA Data and detector representation Data and detector representation Binary (or string) representation Binary (or string) representation Real-valued representation; detectors as hypersphere, or hyper-rectangle Real-valued representation; detectors as hypersphere, or hyper-rectangle Hybrid representation Hybrid representation Generate/elimination mechanism Generate/elimination mechanism Random generation + censoring Random generation + censoring Genetic algorithm Genetic algorithm Greedy algorithm or other deterministic algorithm Greedy algorithm or other deterministic algorithm Matching rule Matching rule Rcb (r contiguous bits) for binary representation Rcb (r contiguous bits) for binary representation Euclidean distance-based for real-valued representation Euclidean distance-based for real-valued representation

8. Is NSA appropriate at all? What we know: What we know: NSA is unique in its process and representation scheme NSA is unique in its process and representation scheme There are some scenarios that NSA has advantage There are some scenarios that NSA has advantage Large number of normal samples Large number of normal samples Negative database to hide sample instances Negative database to hide sample instances What we dont know: What we dont know: What are the applications that NSA always do best in? What are the applications that NSA always do best in?

9. Is real-valued representation appropriate? It is important to choose proper data representation to make it possible to differentiate between classes. It is important to choose proper data representation to make it possible to differentiate between classes. It is application specific. It is application specific. The issue is general in all learning or classification methods. The issue is general in all learning or classification methods. It is NSAs strength that different data representations can fit in. It is NSAs strength that different data representations can fit in.

10. Matching rules influence It is linked with the choice of data representation. It is linked with the choice of data representation. The issues are similar with those of data representation: The issues are similar with those of data representation: It is also application specific. There is no panacea. It is also application specific. There is no panacea. Matching threshold is a real difficulty. Matching threshold is a real difficulty.

11. Positive selection or negative selection? Advantage of positive selection is being more straightforward in many problems. Advantage of positive selection is being more straightforward in many problems. For the same reason, negative selection is more convenient in some cases. For example, the large amount to self samples. For the same reason, negative selection is more convenient in some cases. For example, the large amount to self samples. Common foundation of various negative selection algorithms is better explained with the biological metaphor. Common foundation of various negative selection algorithms is better explained with the biological metaphor. Naïve Self-detector is not a solution. Naïve Self-detector is not a solution.

12. General difficulties not specific in NSA One-class classification One-class classification Without counter examples, the boundary between the two classes is more sensitive to the threshold. Without counter examples, the boundary between the two classes is more sensitive to the threshold. With NSA, a good strategy like boundary-aware V- detector could handle this issue very well in some cases. With NSA, a good strategy like boundary-aware V- detector could handle this issue very well in some cases. High dimensionality High dimensionality 1. How to represent high-dimensional space effectively 2. How many samples are necessary to represent a class With NSA, the first difficulty is to some extent alleviated With NSA, the first difficulty is to some extent alleviated

13. False criticisms of NSA NSA is useless because it doesnt solve curse of dimensionality. NSA is useless because it doesnt solve curse of dimensionality. NSA doesnt work because it failed this experiment. NSA doesnt work because it failed this experiment. NSA make no sense because positive selection is more straightforward in such-and-such case. NSA make no sense because positive selection is more straightforward in such-and-such case. …… ……

14. Experiments 1. The difference between algorithm variations 2. Flexibility of NSA: different distance measure 3. NSAs behavior at high dimensionality

15. Difference between algorithm variations

16. Flexibility of NSA: different distance measure Generalize Euclidean Distance to Minkowski distance of order m (m-norm distance or L-m distance)

17. Different detector shapes resulted

18. NSAs behavior at high dimensionality Setting-up of the experiment Self region A detector

19. 15.65567.88N/A 0.3899.1612 11.29534.62N/A 0.3599.2611 8.06511.11N/A 0.2399.410 3.36502.32N/A 0.2299.69 1.22500.25N/A 0.1499.798 0.4500.04N/A 0.0899.957 050048360.0499.986 050014.0325.670.0599.965 05006.9213.110.0799.944 05001.477.70.011003 SDNumber of detectors SDFalse alarm rate SDDetection rate dimensionality NSAs behavior at high dimensionality The results

20. Conclusion Negative selection algorithms include many variations that are different in many ways. Negative selection algorithms include many variations that are different in many ways. Negative selection algorithms apply to certain scenarios. Negative selection algorithms apply to certain scenarios. There are still many questions to be answered in NSA. There are still many questions to be answered in NSA. Other alternatives exist, but do not replace NSA. Other alternatives exist, but do not replace NSA.

21. Questions and comments? Thank you!