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Erschienen in:
Minimization of Systemic Risk for Directed Network Using Genetic Algorithm Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

The Artificial Immune Ecosystem: A Bio-Inspired Meta-Algorithm for Boosting Time Series Anomaly Detection with Expert Input

verfasst von : Fabio Guigou, Pierre Collet, Pierre Parrend

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

One of the challenges in machine learning, especially in the Big Data era, is to obtain labeled data sets. Indeed, the difficulty of labeling large amounts of data had lead to an increasing reliance on unsupervised classifiers, such as deep autoencoders. In this paper, we study the problem of involving a human expert in the training of a classifier instead of using labeled data. We use anomaly detection in network monitoring as a field of application. We demonstrate how using crude, already existing monitoring software as a heuristic to choose which points to label can boost the classification rate with respect to both the monitoring software and the classifier trained on a fully labeled data set, with a very low computational cost. We introduce the Artificial Immune Ecosystem meta-algorithm as a generic framework integrating the expert, the heuristic and the classifier.

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Vorheriges Kapitel Container Vessel Stowage Planning System Using Genetic Algorithm
Nächstes Kapitel Empirical Analysis of Optimization Methods for the Real-World Dial-a-Ride Problem
Fußnoten
1
Personal experience of the author as a software engineer for a Cloud service provider.
 
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Metadaten
Titel
The Artificial Immune Ecosystem: A Bio-Inspired Meta-Algorithm for Boosting Time Series Anomaly Detection with Expert Input
verfasst von
Fabio Guigou
Pierre Collet
Pierre Parrend
Copyright-Jahr
2017
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-55848-6

Electronic ISBN: 978-3-319-55849-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-55849-3_37

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