Transcript 7. Decision Trees and Decision Rules

國立雲林科技大學
National Yunlin University of Science and Technology
2005.ACM GECCO.8.Discriminating and
visualizing anomalies using negative
selection and self-organizing maps
Advisor : Dr. Hsu
Presenter : Chih-Ling Wang
Author
: Fabio A. Gonzalez, Juan
Carlos Galeano
ACM GECCO 2005
Intelligent Database Systems Lab
Outline
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Motivation
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Objection
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Proper noun
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NS-SOM model structure.
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Experimentation
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Introduction
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Background work
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Conclusion
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My opinion
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Motivation
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The anomaly detection problem could be seen as a classification
problem.
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First, in many real world problems, only normal samples are available at the
training phase.
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Second, the set of possible anomalies could be potentially infinite.
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Objective
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This paper presents a model that can detect anomalies, even when
trained only with normal samples, and can learn from encounters
with new anomalies.
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Proper noun
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Negative Selection Algorithm
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The Negative Selection (NS) algorithm is based on the principles of self/noself discrimination in the immune system.
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It uses as input a set of strings that represents the normal data(self set) in
order to generate detectors in the non-self space. The negative detectors are
chosen by matching them to the self strings: if a detector matches a self string,
it is discarded, otherwise, it is kept.
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NS-SOM model structure
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Experimentation
Unknown
 Iris abnormal
known
abnormal
Primary response
normal
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Secondary response
Confusion matrices for the secondary response
Wisconsin Breast Cancer
Primary response and secondary response
Confusion matrices of the primary and secondary response
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Background work
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Self/non-self discrimination and immune learning
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Artificial immunes systems (AIS) which model the self/non-self discrimination
function of the natural immune system (NIS) are mainly based on the NS
algorithm, nevertheless, new models have been proposed, which are based on
danger theory.
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Both categories of AIS models, self/non-self discrimination based and immune
learning based, have been extensively and independently investigated since the
beginning of AIS research; however, there is not much work on combining
these two approaches in one model.
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Background work (cont.)
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Anomaly visualization
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Usually, the anomaly detection problem arises in context where the monitored
system is very complex in structure and function.
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Information visualization techniques could help to deal with this complexity,
since human perception could detect unexpected features in visual displays and
recall related images to detect anomalies.
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Most of the work done in anomaly visualization has been restricted to the area
of computer security.
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Conclusion
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The model combines a negative selection algorithm and a selforganizing map (SOM) in an immune inspired architecture.
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One remarkable characteristic of the model is its ability to generate
a 2-dimensional visual representation of the feature space.
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This representation facilitates the understanding of the structure of
the self/non-self space by producing a visual discrimination of the
normal, known abnormal and unknown abnormal regions.
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This feature could be useful for building interactive visualization
tools.
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My opinion
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Advantage:Combine AIS mode and immune learning based.
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Disadvantage:Lack the mathematic formula.
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Apply:Anomaly detection.
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