Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data

Positive-Unlabeled (PU) learning presents unique challenges due to the lack of explicitly labeled negative samples, particularly in high-stakes domains such as fraud detection and medical diagnosis. To address data scarcity and privacy constraints, we propose a novel transfer learning with model averaging framework that integrates information from heterogeneous data sources - including fully binary labeled, semi-supervised, and PU data sets - without direct data sharing. For each source domain type, a tailored logistic regression model is conducted, and knowledge is transferred to the PU target domain through model averaging. Optimal weights for combining source models are determined via a cross-validation criterion that minimizes the Kullback-Leibler divergence. We establish theoretical guarantees for weight optimality and convergence, covering both misspecified and correctly specified target models, with further extensions to high-dimensional settings using sparsity-penalized estimators. Extensive simulations and real-world credit risk data analyses demonstrate that our method outperforms other comparative methods in terms of predictive accuracy and robustness, especially under limited labeled data and heterogeneous environments.

Optimized Deployment of Unmanned Aerial Vehicles for Wildfire Detection and Monitoring

In recent years, increased wildfires have caused irreversible damage to forest resources worldwide, threatening wildlives and human living conditions. The lack of accurate frontline information in real-time can pose great risks to firefighters. Though a plethora of machine learning algorithms have been developed to detect wildfires using aerial images and videos captured by drones, there is a lack of methods corresponding to drone deployment. We propose a wildfire rapid response system that optimizes the number and relative positions of drones to achieve full coverage of the whole wildfire area. Trained on the data from historical wildfire events, our model evaluates the possibility of wildfires at different scales and accordingly allocates the resources. It adopts plane geometry to deploy drones while balancing the capability and safety with inequality constrained nonlinear programming. The method can flexibly adapt to different terrains and the dynamic extension of the wildfire area. Lastly, the operation cost under extreme wildfire circumstances can be assessed upon the completion of the deployment. We applied our model to the wildfire data collected from eastern Victoria, Australia, and demonstrated its great potential in the real world.