Active learning is a learning strategy whereby the machine learning algorithm
actively identifies and labels data points to optimize its learning. This
strategy is particularly effective in domains where an abundance of unlabeled
data exists, but the cost of labeling these data points is prohibitively
expensive. In this paper, we consider cases of binary classification, where
acquiring a positive instance incurs a significantly higher cost compared to
that of negative instances. For example, in the financial industry, such as in
money-lending businesses, a defaulted loan constitutes a positive event leading
to substantial financial loss. To address this issue, we propose a shifted
normal distribution sampling function that samples from a wider range than
typical uncertainty sampling. Our simulation underscores that our proposed
sampling function limits both noisy and positive label selection, delivering
between 20% and 32% improved cost efficiency over different test datasets.

主要介绍了主动学习策略在解决二分类问题中的应用，提出了一种基于偏移正态分布采样函数的方法，通过限制噪声和正类标签选择，提高了 20% 到 32% 的成本效率。