论文标题
在分类不平衡的背景下，冷启动积极的学习策略
Cold Start Active Learning Strategies in the Context of Imbalanced Classification
论文作者
论文摘要
我们提出了专门为冷启动阶段提供解决方案的新型积极学习策略，即初始化没有附带标签的大量数据的分类。此外，拟议的策略旨在处理不平衡的上下文，其中随机选择效率高。具体而言，我们的主动学习迭代使用元素分数解决标签稀缺性和不平衡，将从聚类结构提取的信息结合到标签传播模型。通过注释Twitter内容W.R.T.的案例研究说明了该策略。真实洪水事件的证词。我们表明，通过增加少数族裔样本的召回，我们的方法可以有效地应对阶级失衡。
We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached labels. Moreover, proposed strategies are designed to handle an imbalanced context in which random selection is highly inefficient. Specifically, our active learning iterations address label scarcity and imbalance using element scores, combining information extracted from a clustering structure to a label propagation model. The strategy is illustrated by a case study on annotating Twitter content w.r.t. testimonies of a real flood event. We show that our method effectively copes with class imbalance, by boosting the recall of samples from the minority class.
