immune1.ppt

Artificial Immune Systems

Andrew Watkins

Why the Immune System?

• Recognition– Anomaly detection– Noise tolerance

• Robustness• Feature extraction• Diversity• Reinforcement learning• Memory• Distributed• Multi-layered• Adaptive

Definition

AIS are adaptive systems inspired by theoretical immunology and observed

immune functions, principles and models, which are applied to complex problem

domains

(de Castro and Timmis)

Some History

• Developed from the field of theoretical immunology in the mid 1980’s.– Suggested we ‘might look’ at the IS

• 1990 – Bersini first use of immune algos to solve problems

• Forrest et al – Computer Security mid 1990’s

• Hunt et al, mid 1990’s – Machine learning

How does it work?

Immune Pattern Recognition

• The immune recognition is based on the complementarity between the binding region of the receptor and a portion of the antigen called epitope.

• Antibodies present a single type of receptor, antigens might present several epitopes.– This means that different antibodies can recognize a single

antigen

Immune Responses

Antigen Ag1 Antigens Ag1, Ag2

Primary Response Secondary Response

Lag

Response to Ag1

Ant

ibod

y C

once

ntra

tion

Time

Lag

Response to Ag2

Response to Ag1

...

...

Cross-Reactive Response

...

...

Antigen Ag1 + Ag3

Response to Ag1 + Ag3

Lag

Clonal Selection

Immune Network Theory

• Idiotypic network (Jerne, 1974)

• B cells co-stimulate each other– Treat each other a bit like antigens

• Creates an immunological memory

Shape Space Formalism

• Repertoire of the immune system is complete (Perelson, 1989)

• Extensive regions of complementarity

• Some threshold of recognition

V

V

V

V

Self/Non-Self Recognition

• Immune system needs to be able to differentiate between self and non-self cells

• Antigenic encounters may result in cell death, therefore– Some kind of positive selection– Some element of negative selection

General Framework for AIS

Application Domain

Representation

Affinity Measures

Immune Algorithms

Solution

Representation – Shape Space

• Describe the general shape of a molecule

•Describe interactions between molecules

•Degree of binding between molecules

•Complement threshold

Define their Interaction

• Define the term Affinity• Affinity is related to distance

– Euclidian

L

iii AgAbD

1

2)(

• Other distance measures such as Hamming, Manhattan etc. etc.

• Affinity Threshold

Basic Immune Models and Algorithms

• Bone Marrow Models

• Negative Selection Algorithms

• Clonal Selection Algorithm

• Somatic Hypermutation

• Immune Network Models

Bone Marrow Models• Gene libraries are used to create antibodies from the

bone marrow• Use this idea to generate attribute strings that represent

receptors• Antibody production through a random concatenation

from gene libraries

Negative Selection Algorithms

• Forrest 1994: Idea taken from the negative selection of T-cells in the thymus

• Applied initially to computer security• Split into two parts:

– Censoring– Monitoring

Clonal Selection Algorithm (de Castro & von Zuben, 2001)

Randomly initialise a population (P)For each pattern in Ag

Determine affinity to each Ab in PSelect n highest affinity from P

Clone and mutate prop. to affinity with Ag

Add new mutants to P endForSelect highest affinity Ab in P to form part of MReplace n number of random new ones

Until stopping criteria

Immune Network Models (Timmis & Neal, 2001)

Initialise the immune network (P)

For each pattern in Ag

Determine affinity to each Ab in P

Calculate network interaction

Allocate resources to the strongest members of P

Remove weakest Ab in P

EndFor

If termination condition met

exit

else

Clone and mutate each Ab in P (based on a given probability)

Integrate new mutants into P based on affinity

Repeat

Somatic Hypermutation

• Mutation rate in proportion to affinity• Very controlled mutation in the natural immune

system• The greater the antibody affinity the smaller its

mutation rate • Classic trade-off between exploration and

exploitation

How do AIS Compare?

• Basic Components:– AIS B-cell in shape space (e.g. attribute

strings)• Stimulation level

– ANN Neuron• Activation function

– GA chromosome• fitness

Comparing

• Structure (Architecture)– AIS and GA fixed or variable sized

populations, not connected in population based AIS

– ANN and AIS• Do have network based AIS

• ANN typically fixed structure (not always)

• Learning takes place in weights in ANN

Comparing

• Memory– AIS in B-cells

• Network models in connections

– ANN In weights of connections– GA individual chromosome

Comparing

• Adaptation

• Dynamics

• Metadynamics

• Interactions

• Generalisation capabilities

• Etc. many more.

Where are they used?

• Dependable systems

• Scheduling

• Robotics

• Security

• Anomaly detection

• Learning systems

Artificial Immune Recognition System (AIRS):

An Immune-Inspired Supervised Learning Algorithm

AIRS: Immune Principles Employed

• Clonal Selection

• Based initially on immune networks, though found this did not work

• Somatic hypermutation – Eventually

• Recognition regions within shape space

• Antibody/antigen binding

AIRS: Mapping from IS to AIS

• Antibody Feature Vector• Recognition Combination of feature Ball

(RB) vector and vector class• Antigens Training Data• Immune Memory Memory cells—set of

mutated Artificial RBs

Classification• Stimulation of an ARB is based not only on its

affinity to an antigen but also on its class when compared to the class of an antigen

• Allocation of resources to the ARBs also takes into account the ARBs’ classifications when compared to the class of the antigen

• Memory cell hyper-mutation and replacement is based primarily on classification and secondarily on affinity

AIRS Algorithm• Data normalization and initialization• Memory cell identification and ARB

generation• Competition for resources in the

development of a candidate memory cell• Potential introduction of the candidate

memory cell into the set of established memory cells

Memory Cell IdentificationMemory Cell PoolA

ARB Pool

MCmatch FoundMemory Cell PoolA 1

ARB Pool

MCmatch

ARB GenerationMemory Cell PoolA 1

ARB Pool

2

MCmatch

Mutated Offspring

Exposure of ARBs to AntigenMemory Cell PoolA 1

ARB Pool

2

MCmatch

Mutated Offspring3

Development of a Candidate Memory Cell

Memory Cell PoolA 1

ARB Pool

2

MCmatch

Mutated Offspring3

Comparison of MCcandidate and MCmatch

Memory Cell PoolA 1

ARB Pool

2

MCmatch

Mutated Offspring3

MC candidate

4 A

Memory Cell IntroductionMemory Cell PoolA 1

ARB Pool

2

MCmatch

Mutated Offspring3

MCcandidate

45

A

Memory Cells and Antigens

Memory Cells and Antigens

Fisher’s Iris Data SetPima Indians Diabetes

Data Set

Ionosphere Data Set Sonar Data Set

AIRS: Performance Evaluation

Iris Ionosphere Diabetes Sonar

1 Grobian (rough)

100% 3-NN + simplex

98.7% Logdisc 77.7% TAP MFT Bayesian

92.3%

2 SSV 98.0% 3-NN 96.7% IncNet 77.6% Naïve MFT Bayesian 90.4%

3 C-MLP2LN 98.0% IB3 96.7% DIPOL92 77.6% SVM 90.4%

4 PVM 2 rules 98.0% MLP + BP 96.0% Linear Discr. Anal. 77.5%-77.2%

Best 2-layer MLP + BP, 12 hidden

90.4%

5 PVM 1 rule 97.3% AIRS 94.9 SMART 76.8% MLP+BP, 12 hidden 84.7%

6 AIRS 96.7 C4.5 94.9% GTO DT (5xCV) 76.8% MLP+BP, 24 hidden 84.5%

7 FuNe-I 96.7% RIAC 94.6% ASI 76.6% 1-NN, Manhatten 84.2%

8 NEFCLASS 96.7% SVM 93.2% Fischer discr. anal 76.5% AIRS 84.0 9 CART 96.0% Non-linear

perceptron 92.0% MLP+BP 76.4% MLP+BP, 6

hidden 83.5%

10 FUNN 95.7% FSM + rotation

92.8% LVQ 75.8% FSM - methodology?

83.6%

11 1-NN 92.1% LFC 75.8% 1-NN Euclidean 82.2%

12 DB-CART 91.3% RBF 75.7% DB-CART, 10xCV 81.8%

13 Linear perceptron

90.7% NB 75.5-73.8%

CART, 10xCV 67.9%

14 OC1 DT 89.5% kNN, k=22, Manh 75.5%

15 CART 88.9% MML 75.5%

… . . .

22 AIRS 74.1

23 C4.5 73.0%

11 others reported with lower scores, including Bayes, Kohonen, kNN, ID3 …

AIRS: Observations

• ARB Pool formulation was over complicated – Crude visualization– Memory only needs to be maintained in the

Memory Cell Pool

• Mutation Routine– Difference in Quality– Some redundancy

AIRS: Revisions

• Memory Cell Evolution– Only Memory Cell Pool has different classes– ARB Pool only concerned with evolving

memory cells

• Somatic Hypermutation– Cell’s stimulation value indicates range of

mutation possibilities– No longer need to mutate class

Comparisons: Classification Accuracy

• Important to maintain accuracy  AIRS1: Accuracy AIRS2: Accuracy

Iris 96.7 96.0

Ionosphere 94.9 95.6

Diabetes 74.1 74.2

Sonar 84.0 84.9

• Why bother?

Comparisons: Data Reduction

• Increase data reduction—increased efficiency

  Training Set Size AIRS1: Memory Cells AIRS2: Memory Cells

Iris 120 42.1 / 65% 30.9 / 74%

Ionosphere 200 140.7 / 30% 96.3 / 52%

Diabetes 691 470.4 / 32% 273.4 / 60%

Sonar 192 144.6 / 25% 177.7 / 7%

Features of AIRS

• No need to know best architecture to get good results

• Default settings within a few percent of the best it can get

• User-adjustable parameters optimize performance for a given problem set

• Generalization and data reduction

More Information

• http://www.cs.ukc.ac.uk/people/rpg/abw5

• http://www.cs.ukc.ac.uk/people/staff/jt6

• http://www.cs.ukc.ac.uk/aisbook