Collaborative Active Learning in Conditional Trust Environment

In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different data sources and insights without direct data exchange; and (c) it promotes cost-effectiveness and resource efficiency through shared labeling costs. To realize these benefits, we introduce a collaborative active learning framework designed to fulfill the aforementioned objectives. We validate the effectiveness of the proposed framework through simulations. The results demonstrate that collaboration leads to higher AUC scores compared to independent efforts, highlighting the framework's ability to overcome the limitations of individual models. These findings support the use of collaborative approaches in active learning, emphasizing their potential to enhance outcomes through collective expertise and shared resources. Our work provides a foundation for further research on collaborative active learning and its practical applications in various domains where data privacy, cost efficiency, and model performance are critical considerations.

Improving Uncertainty Sampling with Bell Curve Weight Function

Typically, a supervised learning model is trained using passive learning by randomly selecting unlabelled instances to annotate. This approach is effective for learning a model, but can be costly in cases where acquiring labelled instances is expensive. For example, it can be time-consuming to manually identify spam mails (labelled instances) from thousands of emails (unlabelled instances) flooding an inbox during initial data collection. Generally, we answer the above scenario with uncertainty sampling, an active learning method that improves the efficiency of supervised learning by using fewer labelled instances than passive learning. Given an unlabelled data pool, uncertainty sampling queries the labels of instances where the predicted probabilities, p, fall into the uncertainty region, i.e., $p \approx 0.5$. The newly acquired labels are then added to the existing labelled data pool to learn a new model. Nonetheless, the performance of uncertainty sampling is susceptible to the area of unpredictable responses (AUR) and the nature of the dataset. It is difficult to determine whether to use passive learning or uncertainty sampling without prior knowledge of a new dataset. To address this issue, we propose bell curve sampling, which employs a bell curve weight function to acquire new labels. With the bell curve centred at p=0.5, bell curve sampling selects instances whose predicted values are in the uncertainty area most of the time without neglecting the rest. Simulation results show that, most of the time bell curve sampling outperforms uncertainty sampling and passive learning in datasets of different natures and with AUR.

Improve Cost Efficiency of Active Learning over Noisy Dataset

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.