H
Herna L. Viktor
Researcher at University of Ottawa
Publications - 129
Citations - 1795
Herna L. Viktor is an academic researcher from University of Ottawa. The author has contributed to research in topics: Computer science & Relational database. The author has an hindex of 17, co-authored 111 publications receiving 1463 citations. Previous affiliations of Herna L. Viktor include Stellenbosch University & Information Technology University.
Papers
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Journal ArticleDOI
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
Hongyu Guo,Herna L. Viktor +1 more
TL;DR: The approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes, and compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set.
Journal ArticleDOI
Molecular Dynamics, Monte Carlo Simulations, and Langevin Dynamics: A Computational Review
Eric Paquet,Herna L. Viktor +1 more
TL;DR: A computational review of molecular dynamics, Monte Carlo simulations, Langevin dynamics, and free energy calculation is presented to promote a better understanding of the potentialities, limitations, applications, and interrelations of these computational methods.
Book ChapterDOI
Fast Hoeffding Drift Detection Method for Evolving Data Streams
TL;DR: The Fast Hoeffding Drift Detection Method (FHDDM) is introduced which detects the drift points using a sliding window and Hoefding’s inequality and confirms that FHDDM detects drifts with less detection delay, less false positive and less false negative, when compared to the state-of-the-art.
Proceedings ArticleDOI
SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling
TL;DR: The SCUT hybrid sampling method is proposed, which is used to balance the number of training examples in a multi-class setting and, when the SCUT method is used for pre-processing the data before classification, it obtain highly accurate models that compare favourably to the state-of-the-art.
Journal ArticleDOI
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
TL;DR: The Tornado framework is introduced that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms, and the CAR measure is introduced to balance classification, adaptation and resource utilization requirements.