LNCS 2787 – A Danger Theory Inspired Approach to Web Mining 1st Edition by Andrew Secker, Alex Freitas, Jon Timmis ISBN 3540451927 9783540451921

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Product details:

ISBN 10: 3540451927

ISBN 13: 9783540451921

Author: Andrew Secker, Alex A. Freitas, Jon Timmis

A Danger Theory Inspired Approach to Web Mining (LNCS 2787 – Artificial Immune Systems) 1st Edition: Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the relevance of this theory to the application domain of web mining. Central to the idea of Danger theory is that of a context dependant response to invading pathogens. This paper argues that this context dependency could be utilised as powerful metaphor for applications in web mining. An illustrative example adaptive mailbox filter is presented that exploits properties of the immune system, including the Danger theory. This is essentially a dynamical classification task: a task that this paper argues is well suited to the field of artificial immune systems, particularly when drawing inspiration from the Danger theory.

 

LNCS 2787 – A Danger Theory Inspired Approach to Web Mining 1st Edition Table of contents:

1 Introduction

2 Danger Theory Based Learning Model

2.1 Generating Signals

2.2 Classification Using Signals

2.3 The Framework of the DTL Model

3 Filter Spam Using the DTL Model

3.1 Feature Extraction

3.2 Selection of Classifiers

3.3 Performance Measures

4 Experiments of Spam Detection

4.1 The Effects of the Danger Zone

4.2 Comparison Experiments

5 Conclusions

 

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