Augmented Negative Selection Algorithm with Variable-Coverage Detectors

1. Augmented Negative Selection Algorithm with Variable-Coverage Detectors Zhou Ji, St. Jude Children’s Research Hospital Dipankar Dasgupta, The University of Memphis CEC 2004. June 20-23, 2004. Portland, Oregon.

2. Introduction • AIS – Artificial Immune Systems • Major types of AIS: • Negative selection • Immune networks • Clonal Selection • Matching rule is one of the most important components in a negative or positive selection algorithm.

3. Introduction (continued)matching rules • For binary representation: • rcb (r-contiguous bits), • r-chunks, • Hamming distance • For real-valued representation: • Usually based on Euclidean distance or other distance measures

4. Introduction (continued) • By allowing the detectors to have some variable properties, V-detector enhances negative selection algorithm from several aspects: • It takes fewer large detectors to cover non-self region – saving time and space • Small detector covers “holes” better. • Coverage is estimated when the detector set is generated. • The shapes of detectors or even the types of matching rules can be extended to be variable too.

5. Comparison of constant-sized detectors and variable-sized detectors Constant-sized detectors Variable-sized detectors

6. Algorithm (training stage) Generation of constant-sized detectors Generation of variable-sized detectors

7. Outline of the algorithm (generation of variable-sized detector set)

8. Screenshots of the software Message view Visualization of data points and detectors

9. Experiments and Results • Synthetic Data • 2D. Training data are randomly chosen from the normal region. • Fisher’s Iris Data • One of the three types is considered as “normal”. • Biomedical Data • Abnormal data are the medical measures of disease carrier patients. • Pollution Data • Abnormal data are made by artificially altering the normal air measurements

10. Synthetic data - Cross-shaped self spaceShape of self region and example detector coverage (a) Actual self space (b) self radius = 0.05 (c) self radius = 0.1

11. Synthetic data - Cross-shaped self spaceResults Detection rate and false alarm rate Number of detectors

12. Error rates

13. Synthetic data - Ring-shaped self spaceShape of self region and example detector coverage (a) Actual self space (b) self radius = 0.05 (c) self radius = 0.1

14. Synthetic data - Ring-shaped self spaceResults Detection rate and false alarm rate Number of detectors

15. Iris dataComparison with other methods: performance

16. Iris dataComparison with other methods: number of detectors

17. Iris DataVirginica as normal, 50% points used to train Detection rate and false alarm rate Number of detectors

18. Biomedical data • Blood measure for a group of 209 patients • Each patient has four different types of measurement • 75 patients are carriers of a rare genetic disorder. Others are normal.

19. Biomedical data Detection rate and false alarm rate Number of detectors

20. Air pollution data • Totally 60 original records. • Each is 16 different measurements concerning air pollution. • All the real data are considered as normal. • More data are made artificially: • Decide the normal range of each of 16 measurements • Randomly choose a real record • Change three randomly chosen measurements within a larger than normal range • If some the changed measurements are out of range, the record is considered abnormal; otherwise they are considered normal • Totally 1000 records including the original 60 are used as test data. The original 60 are used as training data.

21. Pollution data Detection rate and false alarm rate Number of detectors

22. Conclusion • V-detector’s advantages: • Fewer detectors to achieve similar or better coverage. • Smaller detectors can be used when necessary. • Coverage estimate is included automatically. • Future work: • Variable shape of detectors, variable matching rules • More analysis