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

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

3. Introduction (continued) matching rules For binary representation: For binary representation: rcb (r-contiguous bits), rcb (r-contiguous bits), r-chunks, r-chunks, Hamming distance Hamming distance For real-valued representation: For real-valued representation: Usually based on Euclidean distance or other distance measures 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: 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 It takes fewer large detectors to cover non-self region – saving time and space Small detector covers holes better. Small detector covers holes better. Coverage is estimated when the detector set is generated. 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. 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 Synthetic Data 2D. Training data are randomly chosen from the normal region. 2D. Training data are randomly chosen from the normal region. Fishers Iris Data Fishers Iris Data One of the three types is considered as normal. One of the three types is considered as normal. Biomedical Data Biomedical Data Abnormal data are the medical measures of disease carrier patients. Abnormal data are the medical measures of disease carrier patients. Pollution Data Pollution Data Abnormal data are made by artificially altering the normal air measurements Abnormal data are made by artificially altering the normal air measurements

10. Synthetic data - Cross-shaped self space Shape 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 space Results Detection rate and false alarm rateNumber of detectors

12. Error rates

13. Synthetic data - Ring-shaped self space Shape 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 space Results Detection rate and false alarm rateNumber of detectors

15. Iris data Comparison with other methods: performance Detection rateFalse alarm rate Setosa 100%MILA95.16 0 NSA (single level)100 0 V-detector99.980 Setosa 50%MILA94.02 8.42 NSA (single level)100 11.18 V-detector99.97 1.32 Versicolor 100%MILA84.370 NSA (single level)95.670 V-detector85.950 Versicolor 50%MILA84.4619.6 NSA (single level)9622.2 V-detector88.38.42 Virginica 100%MILA75.750 NSA (single level)92.510 V-detector81.870 Virginica 50%MILA88.9624.98 NSA (single level)97.1833.26 V-detector93.5813.18

16. Iris data Comparison with other methods: number of detectors meanmaxMinSD Setosa 100%204257.87 Setosa 50%16.443355.63 Veriscolor 100%153.242557238.8 Versicolor 50%110.081846022.61 Virginica 100%218.364437866.11 Virginica 50%108.122034630.74

17. Iris Data Virginica as normal, 50% points used to train Detection rate and false alarm rateNumber of detectors

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

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

20. Air pollution data Totally 60 original records. Totally 60 original records. Each is 16 different measurements concerning air pollution. Each is 16 different measurements concerning air pollution. All the real data are considered as normal. All the real data are considered as normal. More data are made artificially: More data are made artificially: 1. Decide the normal range of each of 16 measurements 2. Randomly choose a real record 3. Change three randomly chosen measurements within a larger than normal range 4. 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. 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 rateNumber of detectors

22. Conclusion V-detectors advantages: V-detectors advantages: 1. Fewer detectors to achieve similar or better coverage. 2. Smaller detectors can be used when necessary. 3. Coverage estimate is included automatically. Future work: Future work: Variable shape of detectors, variable matching rules Variable shape of detectors, variable matching rules More analysis More analysis