An Immune System Approach to
Document Classification
Jamie Twycross
Information Infrastructure Laboratory
HP Laboratories Bristol
HPL-2002-288
October 23rd , 2002*
E-mail: [email protected]
artificial
immune
system,
concept
learning,
classifier,
machine
learning,
information
retrieval,
cooperative
coevolution,
feature
extraction
The human immune system as a biological complex adaptive system has
recently provided inspiration for a range of innovative problem solving
techniques in areas such as computer security, knowledge management
and information retrieval. In this dissertation the construction and
performance of a novel immune-based learning algorithm is explored
whose distributed, dynamic and adaptive nature offers many potential
advantages over more traditional models. Through a process of
cooperative coevolution a classifier is generated which consists of a set of
detectors whose local dynamics enable the system as a whole to group
positive and negative examples of a concept. The immune-based learning
algorithm is tested in a rigorous and systematic manner, first on a
standard classification problem and then, combined with an HTML
feature extractor, on a web-based document classification task in the
context of a system which allows users to perform document-based
searches and automatically refine search results. The immune-based
classifier is found to outperform traditional classification paradigms on
both tasks. Further applications in community knowledge management
systems, content filtering, recommendation systems and user profile
generation are also directly relevant to the work presented.
* Internal Accession Date Only
 Copyright Hewlett-Packard Company 2002
Approved for External Publication
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1.00
0.98
0.96
0.94
best classifier
0.92
classifier predictive accuracy
0.90
5
4
3
2
1
classifier size
0
1.0
0.8
0.6
0.4
0.2
species 1 mean predictive accuracy
0.0
1.0
0.8
0.6
0.4
0.2
species 2 mean predictive accuracy
0.0
1.0
0.8
0.6
0.4
0.2
species 3 mean predictive accuracy
0.0
1.0
0.8
0.6
0.4
0.2
species 4 mean predictive accuracy
0.0
1
10
20
30
40
50
60
70
80
90
100
Generation
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GENERALITY BIAS
0.5
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7
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6
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5
4
5
4
3
2
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1
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0.50
0.60
0.70
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Predictive accuracy
1
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0
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0.40
0.50
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Predictive accuracy
0.90
1.00
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10
9
9
8
8
8
7
7
7
6
6
6
4
Frequency
10
9
5
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4
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Predictive accuracy
0.90
1.00
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9
8
8
8
7
7
7
6
6
6
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10
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Frequency
10
9
4
5
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3
2
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2
1
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1.00
0.90
1.0
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0
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Predictive accuracy
4
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5
3
0
1.0
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0
10
5
0.90
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0
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4
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0
10
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Frequency
4
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5
3
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0.70
0.80
Predictive accuracy
mean
0
0.90
1.00
0.40
0.50
0.60
0.70
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