In this work, we propose a novel, drift-aware aggregation method for ensemble systems that dynamically adapts to changing data conditions while maintaining computational efficiency. Our approach leverages an adaptive error smoothing mechanism, a time-adaptive correlation penalty, and a dynamic mixing parameter to integrate a drift-aware weighting scheme that emphasizes recent performance and preserves model diversity. Comprehensive experiments on real-world datasets demonstrate that our method significantly outperforms traditional aggregation techniques and state-of-the-art algorithms, particularly in environments characterized by recurring concept drift and data scarcity. These results underscore the potential of our approach as a practical and scalable solution for adaptive ensemble aggregation in dynamic, resource-constrained settings.
As e-commerce continues to grow rapidly, the threat of online fraud has become a critical concern, posing significant risks to businesses and consumers alike. Traditional fraud detection systems struggle to keep pace with the evolving strategies of fraudsters, especially given the challenges of high-dimensional data, class imbalance, and limited interpretability. In this work, we introduce LEMON, a Light and Efficient fraMework for e-cOmmerce Fraud DetectioN, that uses Discriminating Code Set (DCS) in graph theory to attain an intrinsic set of features for enhancing the performance and efficiency of fraud detection models. We evaluate LEMON on the IEEE-CIS Fraud Detection dataset, comparing it with baseline techniques including manual feature engineering (Kaggle Top-1), XGBoost-based feature importance, and a hybrid LEMON+XGBoost model. Our results show that the approach not only achieves the highest ROC-AUC (0.976) and F1-score (0.78) but also significantly reduces training time. These findings highlight the effectiveness of DCS in selecting a compact, highly discriminative feature subset that improves fraud detection in real-world, large-scale e-commerce systems.
Mining frequent closed patterns is a fundamental task in both frequent pattern mining and association rule mining. In this research, we propose two efficient algorithms, tTRK-CloITP and dTRK-CloITP, for mining top‑rank‑k frequent closed inter‑transaction patterns (FCITPs) using tidset and diffset structures, respectively. Our approach offers several key contributions. First, the database is scanned once to generate 1‑patterns along with their corresponding tidsets. From this set of 1‑patterns, inter‑transaction 2‑patterns are generated using either the tidset or diffset structure without requiring another scan of the entire mega‑transaction database. This enables efficient inter‑transaction pattern generation starting from the 2‑pattern level. A depth‑first search (DFS) traversal is then used to iteratively generate (l + 1)‑patterns from l‑patterns. Second, we introduce additional pruning techniques based on either the tidset or diffset structure to efficiently identify closed patterns when inserting them into the top‑rank‑k table, which contains the complete set of top‑rank‑k frequent closed inter‑transaction patterns. Finally, we conduct extensive experiments on benchmark datasets from the FIMI repository ( http://fimi.uantwerpen.be/data/ ) to demonstrate the effectiveness and efficiency of the proposed algorithms in terms of both runtime and memory usage.
This paper describes a system in which an agent can find successful action strategies in the Wumpus World—a classic environment for testing logical reasoning. Despite such successful strategies can be fully described and implemented using first-order logic, it is challenging for reinforcement learning algorithms to automatically find them because of partially observable states, sparse rewards and the logic-based nature of the problem, which characterizes the Wumpus World environment. Our solution consists of the design of sensation maps, where partial observations are accumulated, shaping the reward function, custom two-stage ϵ -greedy action selection strategy, and curriculum learning. With these components, we were able to train agents using just a basic DQN algorithm—the pioneer of deep reinforcement learning. Our experiments confirm the good performance of the developed method improving the results described in the literature. Our solution brings reinforcement learning-based approaches closer to the complete solution of the problem designed to be solved using first-order logic.
In this work we tackle the problem of the data quality and labeling in the machine learning task of detecting fake news and disinformation. The major contribution of this paper is the new proposition to use large language models as an additional annotation mechanism in order to enrich the dataset and model with the new information. In this manner, we are able to faster annotate new content, and limit so called aging effect of the models. Hereby, we also evaluate our approach and provide promising results.
