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Lecture 11
Lecture 11
Collaborative Bio-Inspired Algorithms Lecture 11 : Clonal Selection - - PowerPoint PPT Presentation
Collaborative Bio-Inspired Algorithms Lecture 11 : Clonal Selection - - PowerPoint PPT Presentation
Lecture 11 Collaborative Bio-Inspired Algorithms Lecture 11 : Clonal Selection Algorithms Prof Jon Timmis November 9, 2009 Lecture 11 Outline Quick Primer Static Clonal Selection AIRS Dynamic Clonal Selection AISEC Summary Lecture 11
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Lecture 11 Quick Primer
Quick Primer
◮ Supervised machine learning ◮ When you know the class of an instance before you start ◮ Wish to build a model of that data so you can then classify
instances you have not seen before
◮ Many approaches including:
◮ Neural networks, rule induction, Bayesian, ILP . . .
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Lecture 11 Quick Primer
Learning in the Immune System
◮ Recall the learning and memory capabilities in the clonal
selection process
◮ Exploit this local v global search in a learning context ◮ Evolve a set of detectors that can generalise well enough
to classify unseen data items
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Lecture 11 Static Clonal Selection AIRS
Artificial Immune Recognition System (AIRS)
◮ Artificial Immune Recognition System (AIRS) [2] ◮ Uses the concepts of ARB’s (Artificial Recognition Balls) ◮ Resource based competition for survival in order to control
the population
◮ One-shot learning system
Go to the board and describe algorithm . . .
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Lecture 11 Static Clonal Selection AIRS
AIRS Results
Data Set Accuracy Iris 96% Ionosphere 95.6% Diabetes 74.2% Sonar 84.9%
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Lecture 11 Dynamic Clonal Selection AISEC
Continuous Learning
◮ Used when you want to classify changes over time (the
notion of what is in a class)
◮ Levels of what you are interested in may change over time
- r the context of where you are working or what you are
doing
◮ Web content mining is a perfect testbed for these ideas ◮ This study looked at email classification of interesting v
un-interesting
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Lecture 11 Dynamic Clonal Selection AISEC
Email Filtering
◮ Dynamic supervised classification algorithm
◮ E-mail classified as interesting and uninteresting ◮ Uses constant feedback from user ◮ Capable of continuous adaptation ◮ This tracks concept drift and can also handle concept shift
◮ Representation: Subject, Sender and Return address
(based on existing literature, this is all you really need)
◮ Affinity measure: Proportion of words found in one cell
compared to another (very naive)
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Lecture 11 Dynamic Clonal Selection AISEC
AISEC I
Memory cells Naive cells
Figure: (1) System is initialised with uninteresting emails Figure: (2) Email classified as uninteresting if high stimualtion
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Lecture 11 Dynamic Clonal Selection AISEC
AISEC II
Classification Region Stimulation Region
Figure: (3) Highly stimulated cell reproduces, as in clonal selection Figure: (4) Highest affinity cell rewarded through promotion to memory cell
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Lecture 11 Dynamic Clonal Selection AISEC
AISEC II
Figure: (5) Any cell responsible for incorrect classification is removed Figure: (6) Aged cells (and un-stimulated) die
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Lecture 11 Dynamic Clonal Selection AISEC
Results
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2268 e-mails (742 uninteresting) received over 6 months [1]
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E-mails presented in chronological order
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Feedback given after EVERY classification and AISEC run 10 times
Technique Accuracy C5 83.9% Naive Baysian (static) 85% Neural network 85.6% AISEC (static) 86% Naive Baysian (dynamic) 88.05% AISEC (dynamic) 89.05%
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Lecture 11 Dynamic Clonal Selection AISEC
Results
75% 77% 79% 81% 83% 85% 87% 89% 91% 93% 95% 97% 99% 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200
Number of e-mails classified Classification Accuracy
AISEC Bayesian
Figure: Classification accuracy over time
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Lecture 11 Summary
Summary
◮ Learning in the immune system ◮ Static clonal selection: AIRS ◮ Dynamic clonal selection: AISEC ◮ Many other variants, not covered here. Read the
supporting literature.
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Lecture 11 Summary
- A. Secker, A. Freitas, and J. Timmis.
AISEC an artificial immune system for email classification. In Proceedings of the Congress on Evolutionary Computation, pages 131–139, 2003.
- A. Watkins, J. Timmis, and L. Boggess.