Minimizing Label Complexity with Active Learning in Machine Learning

In this article, the authors present several strategies to evaluate machine learning models in online learning scenarios where limited labels are available. The objective is to minimize regret, which is the difference between the model’s loss and the loss obtained by each expert i.
Firstly, the authors introduce the concept of label complexity, which refers to the number of labels used to describe a given set of examples. They propose an evaluation metric called expected label complexity (E[R(T )]), which measures the expected number of labels required to accurately classify the examples in the dataset over time.
Next, the authors discuss several strategies for updating the version space and computing the region of disagreement at each round t. These strategies include using all collected labels, selecting a subset of labels based on their uncertainty, or adapting the version space according to the expert’s performance. The objective is to balance the trade-off between exploration and exploitation by updating the version space appropriately while minimizing regret.
The authors also introduce the concept of experts, which are hypotheses in the hypothesis space H. At each round t, the learner selects an expert i at random, and the loss associated with this expert is computed. The objective is to minimize the cumulative regret over time, which measures the difference between the learner’s loss and the loss obtained by each expert.
To evaluate these strategies, the authors use a theoretical framework based on the concept of Vapnik-Chervonenkis (VC) dimension, which measures the capacity of a hypothesis space. The VC dimension provides a bound on the expected regret, which can be used to compare different strategies for updating the version space and computing the region of disagreement.
Finally, the authors demonstrate the effectiveness of their proposed strategies through simulations using several real-world datasets. They show that their approach can significantly reduce the cumulative regret compared to other state-of-the-art methods.
In summary, this article provides a comprehensive analysis of evaluation strategies for machine learning in online learning scenarios with limited labels. The authors present several effective strategies to minimize regret while balancing exploration and exploitation, and they provide theoretical guarantees on the performance of these strategies using the VC dimension framework. The proposed approach can be useful in practical applications where limited labels are available, such as in recommendation systems or fraud detection.