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 advantage and disadvantage 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.
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