移动众包中的个性化分类器包围剪枝框架
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
摘要：
包围学习已经被广泛应用于移动应用程序，从环境感知到活动识别。集成学习的一个基本问题是分类精度和计算成本之间的权衡，这是集成修剪的目标。在众包过程中，集中式聚合器将集成学习模型发布给大量移动参与者进行任务评估或作为众包学习结果，而不同的参与者可能会寻求不同程度的准确性-成本权衡。然而，大多数现有的集成修剪方法只考虑这种权衡的一个相同水平。在这项研究中，我们提出了一个有效的合奏修剪框架，通过多目标优化个性化的准确性成本权衡。具体来说，对于常用的线性组合风格的权衡，我们提供了一个客观的混合优化，以进一步减少合奏候选人的数量。实验结果表明，我们的框架是高效的个性化集成修剪，并实现更好的修剪性能与目标混合优化相比，国家的最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
