Impact of Biased Non-Response on Active Learning: A New Correction Algorithm

In this article, Huang et al. propose a new active learning strategy called "active learning by querying informative and representative examples." The authors aim to improve the efficiency and accuracy of machine learning algorithms by selectively querying the most informative and representative instances for labeling.
The authors explain that traditional active learning methods often rely on random sampling or uncertainty sampling, which can lead to inefficient use of labeling resources. In contrast, their proposed method uses a combination of informativeness and representativeness to identify the most valuable instances for labeling.
The authors define informativeness as the potential impact of an instance on the learning process, based on its similarity to previously labeled instances. They propose using a distance metric, such as cosine similarity or Jaccard similarity, to measure the similarity between instances.
Representativeness is defined as the ability of an instance to represent the underlying patterns in the data. The authors suggest using a clustering algorithm, such as k-means or hierarchical clustering, to group similar instances and identify representative instances.
To combine informativeness and representativeness, the authors propose a weighted sampling strategy. They assign higher weights to instances that are both informative and representative, and lower weights to instances that are only informative or only representative. This approach allows the algorithm to focus on the most valuable instances for labeling.
The authors evaluate their proposed method using several experiments on a text classification task. Their results show that active learning by querying informative and representative examples can significantly reduce the number of instances needed for accurate classification, while also improving the efficiency of the labeling process.
In summary, Huang et al.’s article proposes an innovative active learning strategy that uses a combination of informativeness and representativeness to identify the most valuable instances for labeling. By selectively querying these instances, the algorithm can reduce the number of instances needed for accurate classification while improving the efficiency of the labeling process. This approach has important implications for applications where labeling data is time-consuming or expensive, such as in medical diagnosis or financial forecasting.