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Machine Learning

, Volume 77, Issue 1, pp 61–102 | Cite as

Anytime classification for a pool of instances

  • Bei Hui
  • Ying Yang
  • Geoffrey I. Webb
Article

Abstract

In many real-world applications of classification learning, such as credit card transaction vetting or classification embedded in sensor nodes, multiple instances simultaneously require classification under computational resource constraints such as limited time or limited battery capacity. In such a situation, available computational resources should be allocated across the instances in order to optimize the overall classification efficacy and efficiency. We propose a novel anytime classification framework, Scheduling Anytime Averaged Probabilistic Estimators (SAAPE), which is capable of classifying a pool of instances, delivering accurate results whenever interrupted and optimizing the collective classification performance. Following the practice of our previous anytime classification system AAPE, SAAPE runs a sequence of very efficient Bayesian probabilistic classifiers to classify each single instance. Furthermore, SAAPE implements seven alternative scheduling schemes to decide which instance gets available computational resources next such that a new classifier can be applied to refine its classification. We formally present each scheduling scheme’s definition, rationale and time complexity. We conduct large-scale experiments using 60 benchmark data sets and diversified statistical tests to evaluate SAAPE’s performance on zero-one loss classification as well as on probability estimation. We analyze each scheduling scheme’s advantages and disadvantages according to both theoretical understandings and empirical observations. Consequently we identify effective scheduling schemes that enable SAAPE to accomplish accurate anytime classification for a pool of instances.

Keywords

Anytime classification Computational resource constraints Bayesian probabilistic classifiers Ensemble learning 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengDuChina
  2. 2.Australian Taxation OfficeMelbourneAustralia
  3. 3.Clayton School of Information TechnologyMonash UniversityClaytonAustralia

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