21 citations · 35 across the 9 of their papers we have counts for
9 papers
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection
Caroline Strickland, Chandrika Saha, Muhammad Zakar +4
Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detec…
Hybrid Feature- and Similarity-Based Models for Joint Prediction and Interpretation
Jacqueline K. Kueper, Jennifer Rayner, Daniel J. Lizotte
Electronic health records (EHRs) include simple features like patient age together with more complex data like care history that are informative but not easily represented as indiv…
Decision-Directed Data Decomposition
Brent D. Davis, Ethan Jackson, Daniel J. Lizotte
We present an algorithm, Decision-Directed Data Decomposition (D4), which decomposes a dataset into two components. The first contains most of the useful information for a specifie…
FRAMR-EMR: Framework for Prognostic Predictive Model Development Using Electronic Medical Record Data with a Case Study in Osteoarthritis Risk
Jason Black, Amanda Terry, Daniel Lizotte
Background-Prognostic predictive models are used in the delivery of primary care to estimate a patients risk of future disease development. Electronic medical record, EMR, data can…
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity
Rhiannon V. Rose, Daniel J. Lizotte
When faced with a supervised learning problem, we hope to have rich enough data to build a model that predicts future instances well. However, in practice, problems can exhibit pre…
Generative Multiple-Instance Learning Models For Quantitative Electromyography
Tameem Adel, Benn Smith, Ruth Urner +2
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped to…