  Ensemble learning has been widely employed by mobile applications, ranging
from environmental sensing to activity recognitions. One of the fundamental
issue in ensemble learning is the trade-off between classification accuracy and
computational costs, which is the goal of ensemble pruning. During
crowdsourcing, the centralized aggregator releases ensemble learning models to
a large number of mobile participants for task evaluation or as the
crowdsourcing learning results, while different participants may seek for
different levels of the accuracy-cost trade-off. However, most of existing
ensemble pruning approaches consider only one identical level of such
trade-off. In this study, we present an efficient ensemble pruning framework
for personalized accuracy-cost trade-offs via multi-objective optimization.
Specifically, for the commonly used linear-combination style of the trade-off,
we provide an objective-mixture optimization to further reduce the number of
ensemble candidates. Experimental results show that our framework is highly
efficient for personalized ensemble pruning, and achieves much better pruning
performance with objective-mixture optimization when compared to state-of-art
approaches.
