Multi-instance multi-label active learning (MIMAL) usually uses example uncertainty and label correlation to select the most valuable example-label pairs, maximizing the learner’s performance. However, the existing MIMAL solutions do not consider the correlation of example features when selecting example-label pairs. Here, this paper proposes a novel MIMAL framework that can effectively exploit the relationship between examples and features to reduce annotation cost. We first perform feature screening on the examples. It effectively eliminates the interference of useless features on the example to the annotations. Next, we quantify the correlation between features and examples as the basis for selecting example-label pairs. Finally, we query for the most likely positive subexample-label pair among the selected example-label pairs. The extensive experiments on multi-label datasets from diverse domains show that the proposed MIMAL can better save query cost and achieve superior performance than state-of-the-art MIMAL methods.

Active learning to select the most valuable examples for annotation from unlabeled data pools in high-dimensional datasets is particularly challenging and has attracted considerable attention. However, the impact of annotated examples on global data was often not considered. In this paper, we propose a novel batch active learning strategy called TA-GCNAL based on the perspective of task model. To consider data distribution in the labeled and unlabeled pool, we first calculate the correlation between data through a graph convolution network to predict the example’s uncertainty. Then, a subset of examples with high uncertainty is generated to replace the unlabeled data pools. It transformed into a probability space based on the task model for core dataset selection and annotation. Finally, we compute the representativeness of the core dataset in probability space for the example batch selection, taking into account the correlation between labeled and unlabeled pools. Our proposed TA-GCNAL achieve superior performance than the state-of-the-art methods on various benchmark datasets. The improvement is particularly significant on unbalanced and large number of classes datasets.

Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.