A5042405413- Machine Learning and Data Classification
- Data Stream Mining Techniques
- Transportation and Mobility Innovations
- Network Security and Intrusion Detection
- Imbalanced Data Classification Techniques
- Recommender Systems and Techniques
- Spam and Phishing Detection
- Human Mobility and Location-Based Analysis
- Anomaly Detection Techniques and Applications
- Explainable Artificial Intelligence (XAI)
Chongqing University
2023
IBM (United States)
2003-2006
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume streaming data, drifts. In this paper, we propose general framework concept-drifting using weighted ensemble classifiers. We train classification models, such as...
Next point-of-interest (POI) recommendation optimizes user travel experiences and enhances platform revenues by providing users with potentially appealing next location choices. In recent research, scholars have successfully mined users' general tastes varying interests modeling long-term short-term check-in sequences. However, conventional methods for long predominantly employ distinct encoders to process interaction data independently, disparities in limiting the ultimate performance of...
There has been increasing number of independently proposed randomization methods in different stages decision tree construction to build multiple trees. Randomized have reported be significantly more accurate than widely-accepted single trees, although the training procedure some incorporates a surprisingly random factor and therefore opposes generally accepted idea employing gain functions choose optimum features at each node compute that fits data. One important question is not well...