Improved TrAdaBoost and its application to transaction fraud detection
L Zheng, G Liu, C Yan, C Jiang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
AdaBoost is a boosting-based machine learning method under the assumption that the data
in training and testing sets have the same distribution and input feature space. It increases …
in training and testing sets have the same distribution and input feature space. It increases …
Temporal prediction of multiapplication consolidated workloads in distributed clouds
With their fast development and deployment, a large number of cloud services provided by
distributed cloud data centers have become the most important part of Internet services. In …
distributed cloud data centers have become the most important part of Internet services. In …
[HTML][HTML] Structural break-aware pairs trading strategy using deep reinforcement learning
JY Lu, HC Lai, WY Shih, YF Chen, SH Huang… - The Journal of …, 2022 - Springer
Pairs trading is an effective statistical arbitrage strategy considering the spread of paired
stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break …
stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break …
SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers
Nowadays, a large number of cloud services have been published and hosted by geo-
distributed cloud data centers (Geo-2DCs). In spite of numerous benefits, those Geo-2DCs …
distributed cloud data centers (Geo-2DCs). In spite of numerous benefits, those Geo-2DCs …
A novel technique for behavioral analytics using ensemble learning algorithms in E-commerce
M Alojail, S Bhatia - IEEE Access, 2020 - ieeexplore.ieee.org
The era of E-commerce and availability of data in every field of operations in an enormous
volume that implies to Big Data is one of the biggest sources of competitive advantage for …
volume that implies to Big Data is one of the biggest sources of competitive advantage for …
Multiscale drift detection test to enable fast learning in nonstationary environments
A model can be easily influenced by unseen factors in nonstationary environments and fail
to fit dynamic data distribution. In a classification scenario, this is known as a concept drift …
to fit dynamic data distribution. In a classification scenario, this is known as a concept drift …
Drifted Twitter spam classification using multiscale detection test on KL divergence
Twitter spam classification is a tough challenge for social media platforms and cyber security
companies. Twitter spam with illegal links may evolve over time in order to deceive filtering …
companies. Twitter spam with illegal links may evolve over time in order to deceive filtering …
Overlapping community change-point detection in an evolving network
Change-point detection is a task that looks for specific moments across which a network
changes fundamentally. Change-point detection is one of the most important challenges for …
changes fundamentally. Change-point detection is one of the most important challenges for …
Modeling sequential listening behaviors with attentive temporal point process for next and next new music recommendation
Recommender systems, which aim to provide personalized suggestions for users, have
proven to be an effective approach to cope with the information overload problem existing in …
proven to be an effective approach to cope with the information overload problem existing in …
Time-dependent cloud workload forecasting via multi-task learning
Cloud services have rapidly grown in cloud data centers (CDCs). Accurate workload
prediction benefits CDCs since appropriate resource provisioning can be performed for their …
prediction benefits CDCs since appropriate resource provisioning can be performed for their …