Shanshan Wang
Beihang University
20 Papers
42 Citations
Shanshan Wang is an academic researcher from Beihang University. The author has contributed to research in topics: Autoregressive model & Linear model. The author has an hindex of 6, co-authored 20 publications.
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Papers
An effective intrusion detection framework based on SVM with feature augmentation
Huiwen Wang,Jie Gu,Shanshan Wang +2 more
TL;DR: This paper implements the logarithm marginal density ratios transformation to form the original features with the goal of obtaining new and better-quality transformed features that can greatly improve the detection capability of an SVM-based detection model.
270
A novel approach to intrusion detection using SVM ensemble with feature augmentation
TL;DR: An effective intrusion detection framework based on SVM ensemble with feature augmentation based on marginal density ratios is proposed, which possesses huge competitive advantages when compared to other existing methods in terms of accuracy, detection rate, false alarm rate and training speed.
169
Statistical regression modeling for energy consumption in wastewater treatment.
TL;DR: The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.
34
Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19
TL;DR: A flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.
16
Convex clustering method for compositional data via sparse group lasso
TL;DR: A compositional clustering framework based on convex clustering, which is a convex relaxation of hierarchical clustering that incorporates a fused penalty term on the cluster prototypes that effectively selects informative features and promotes within-feature sparsity is developed.
11