DARC: Timely classification with randomly delayed features

Jie Xu, Qi Cai, Cong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Many emerging Big Data applications involve real- time classification in which data instances arriving sequentially over time need to be classified based on their feature vectors. A common and implicit assumption in existing works is that the features become available instantly with the instance and simultaneously with each other, which, however, rarely holds in practice. Instead, features of an instance may experience various random delays to be available. In such scenarios, an important trade-off emerges between accurate classification and timely classification. In this paper, we provide a first formulation of this important problem and propose efficient online algorithms, namely DAlay-aware Real-time Classification (DARC) algorithms, that maximize the classification accuracy given an average classification delay constraint. The algorithms are developed based on the Lyapunov stochastic optimization technique which provides strong performance guarantee. Numerical results on an intrusion detection dataset are provided to show the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013289
StatePublished - Feb 2 2017
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: Dec 4 2016Dec 8 2016


Other59th IEEE Global Communications Conference, GLOBECOM 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality


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