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

  • Fabio Guigou
  • Pierre Collet
  • Pierre Parrend
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


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.


Artificial immune system Boosting Anomaly detection Time series Machine learning 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabio Guigou
    • 1
    • 2
    • 4
  • Pierre Collet
    • 2
    • 4
  • Pierre Parrend
    • 2
    • 3
    • 4
  1. 1.IPLineCaluire-et-cuireFrance
  2. 2.ICube LaboratoryUniversité de StrasbourgStrasbourgFrance
  3. 3.ECAM Strasbourg-EuropeSchiltigheimFrance
  4. 4.Complex System Digital Campus (UNESCO Unitwin)ParisFrance

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