In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's performance while minimizing the number of requests. In this paper, we propose a new algorithm (PAL-ACS) that transforms the ACS problem into an active learning task by introducing pseudo instances. These are used to estimate the usefulness of an upcoming instance for each class using the performance gain model from probabilistic active learning. Our experimental evaluation (on synthetic and real data) shows the advantages of our algorithm compared to state-of-the-art algorithms. It effectively prefers the sampling of difficult classes and thereby improves the classification performance.
翻译:在机器学习中,主动级选(ACS)算法(ACS)旨在积极选择一个类,并请神器为该类提供优化分类员业绩的范例,同时尽量减少请求的数量。在本文中,我们建议采用一种新的算法(PAL-ACS),通过引入假例,将ACS问题转化为积极的学习任务。这些算法用来估计利用概率性积极学习的性能增益模型为每个类选例的用处。我们的实验评估(合成和真实数据)显示了我们算法相对于最新算法的优势。它实际上更倾向于对困难类进行抽样,从而改进分类性能。