augmented negative selection algorithm with variable-coverage detectors zhou ji, zhou ji, st. jude...
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Augmented Negative Augmented Negative Selection Algorithm with Selection Algorithm with
Variable-Coverage Variable-Coverage DetectorsDetectors
Zhou Ji, Zhou Ji, St. Jude Children’s Research Hospital
Dipankar Dasgupta, Dipankar Dasgupta, The University of Memphis
CEC 2004. June 20-23, 2004. Portland, Oregon.
IntroductionIntroduction
AIS – Artificial Immune SystemsAIS – Artificial Immune Systems Major types of AIS:Major types of AIS:
Negative selectionNegative selection Immune networksImmune networks Clonal SelectionClonal Selection
Matching rule is one of the most important Matching rule is one of the most important components in a negative or positive components in a negative or positive selection algorithm. selection algorithm.
Introduction (continued)Introduction (continued)matching rulesmatching 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 Usually based on Euclidean distance or other
distance measuresdistance measures
Introduction (continued)Introduction (continued)
By allowing the detectors to have some variable By allowing the detectors to have some variable properties, properties, V-detectorV-detector enhances negative enhances negative selection algorithm from several aspects:selection algorithm from several aspects: It takes fewer large detectors to cover non-self region It takes fewer large detectors to cover non-self region
– saving time and space– saving time and space Small detector covers “holes” better.Small detector covers “holes” better. Coverage is estimated when the detector set is Coverage is estimated when the detector set is
generated.generated. The shapes of detectors or even the types of The shapes of detectors or even the types of
matching rules can be extended to be variable matching rules can be extended to be variable too.too.
Comparison of constant-sized detectors Comparison of constant-sized detectors and variable-sized detectorsand variable-sized detectors
Constant-sized detectors Variable-sized detectors
Algorithm (training stage)Algorithm (training stage)
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Generation of constant-sized detectors
Generation of variable-sized detectors
Outline of the algorithm Outline of the algorithm (generation of variable-sized detector set)(generation of variable-sized detector set)
Screenshots of the softwareScreenshots of the software
Message view Visualization of data points and detectors
Experiments and ResultsExperiments and Results
Synthetic DataSynthetic Data 2D. Training data are randomly chosen from the 2D. Training data are randomly chosen from the
normal region.normal region. Fisher’s Iris DataFisher’s Iris Data
One of the three types is considered as “normal”.One of the three types is considered as “normal”. Biomedical DataBiomedical Data
Abnormal data are the medical measures of disease Abnormal data are the medical measures of disease carrier patients.carrier patients.
Pollution DataPollution Data Abnormal data are made by artificially altering the Abnormal data are made by artificially altering the
normal air measurementsnormal air measurements
Synthetic data - Synthetic data - Cross-shaped self spaceCross-shaped self space Shape of self region and example detector coverageShape of self region and example detector coverage
(a) Actual self space (b) self radius = 0.05 (c) self radius = 0.1
Synthetic data - Synthetic data - Cross-shaped self spaceCross-shaped self space ResultsResults
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Detection rate and false alarm rate Number of detectors
Error ratesError rates
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false negative (99% coverage) false positive (99% coverage)
Synthetic data - Synthetic data - Ring-shaped self spaceRing-shaped self space Shape of self region and example detector coverageShape of self region and example detector coverage
(a) Actual self space (b) self radius = 0.05 (c) self radius = 0.1
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Detection rate (99.99% coverage) Detection rate (99% coverage)False alarm rate (99% coverage) False alarm rate (99.99% coverage)
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Synthetic data - Synthetic data - Ring-shaped self spaceRing-shaped self space ResultsResults
Detection rate and false alarm rate Number of detectors
Iris dataIris dataComparison with other methods: performanceComparison with other methods: performance
Detection rate False alarm rate
Setosa 100% MILA 95.16 0
NSA (single level) 100 0
V-detector 99.98 0
Setosa 50% MILA 94.02 8.42
NSA (single level) 100 11.18
V-detector 99.97 1.32
Versicolor 100% MILA 84.37 0
NSA (single level) 95.67 0
V-detector 85.95 0
Versicolor 50% MILA 84.46 19.6
NSA (single level) 96 22.2
V-detector 88.3 8.42
Virginica 100% MILA 75.75 0
NSA (single level) 92.51 0
V-detector 81.87 0
Virginica 50% MILA 88.96 24.98
NSA (single level) 97.18 33.26
V-detector 93.58 13.18
Iris dataIris dataComparison with other methods: number of detectorsComparison with other methods: number of detectors
mean max Min SD
Setosa 100% 20 42 5 7.87
Setosa 50% 16.44 33 5 5.63
Veriscolor 100% 153.24 255 72 38.8
Versicolor 50% 110.08 184 60 22.61
Virginica 100% 218.36 443 78 66.11
Virginica 50% 108.12 203 46 30.74
Iris DataIris DataVirginica as normal, 50% points used to trainVirginica as normal, 50% points used to train
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Detection rate (99.99% coverage) Detection rate (99% coverage)False alarm rate (99% coverage) False alarm rate (99.99% coverage)
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Biomedical dataBiomedical data
Blood measure for a group of 209 patientsBlood measure for a group of 209 patients Each patient has four different types of Each patient has four different types of
measurementmeasurement 75 patients are carriers of a rare genetic 75 patients are carriers of a rare genetic
disorder. Others are normal.disorder. Others are normal.
Biomedical data Biomedical data
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Detection rate and false alarm rate Number of detectors
Air pollution dataAir pollution data Totally 60 original records.Totally 60 original records. Each is 16 different measurements concerning air Each is 16 different measurements concerning air
pollution.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:
1.1. Decide the normal range of each of 16 measurementsDecide the normal range of each of 16 measurements2.2. Randomly choose a real recordRandomly choose a real record3.3. Change three randomly chosen measurements within a larger Change three randomly chosen measurements within a larger
than normal rangethan normal range4.4. If some the changed measurements are out of range, the If some the changed measurements are out of range, the
record is considered abnormal; otherwise they are considered record is considered abnormal; otherwise they are considered normalnormal
Totally 1000 records including the original 60 are used Totally 1000 records including the original 60 are used as test data. The original 60 are used as training data.as test data. The original 60 are used as training data.
Pollution dataPollution data
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ConclusionConclusion
V-detectorV-detector’s advantages:’s advantages:
1.1. Fewer detectors to achieve similar or better Fewer detectors to achieve similar or better coverage.coverage.
2.2. Smaller detectors can be used when necessary.Smaller detectors can be used when necessary.
3.3. Coverage estimate is included automatically.Coverage estimate is included automatically. Future work:Future work:
Variable shape of detectors, variable matching rulesVariable shape of detectors, variable matching rules More analysisMore analysis