Skip to main content
Article thumbnail
Location of Repository

Data Sniffing – Monitoring of Machine Learning for Online Adaptive Systems

By Yan Liu, Tim Menzies and Bojan Cukic

Abstract

Adaptive systems are systems whose function evolves while adapting to current environmental conditions. Due to the real-time adaptation, newly learned data have a significant impact on system behavior. When online adaptation is included in system control, anomalies could cause abrupt loss of system functionality and possibly result in a failure. In this paper we present a framework for reasoning about the online adaptation problem. We describe a machine learning tool that sniffs data and detects anomalies before they are passed to the adaptive components for learning. Anomaly detection is based on distance computation. An algorithm for framework evaluation as well as sample implementation and empirical results are discussed. The method we propose is simple and reasonably effective, thus it can be easily adopted for testing.

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.8743
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://menzies.us/pdf/03datasn... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.