Online nonlinear AUC maximization for imbalanced data sets
Classifying binary imbalanced streaming data is a significant task in both machine learning
and data mining. Previously, online area under the receiver operating characteristic (ROC) …
and data mining. Previously, online area under the receiver operating characteristic (ROC) …
[HTML][HTML] Stochastic AUC optimization algorithms with linear convergence
Area under the ROC curve (AUC) is a standard metric that is used to measure classification
performance for imbalanced class data. Developing stochastic learning algorithms that …
performance for imbalanced class data. Developing stochastic learning algorithms that …
A sparse nonlinear classifier design using AUC optimization
AUC (Area under the ROC curve) is an important performance measure for applications
where the data is highly imbalanced. Efficient AUC optimization is a challenging research …
where the data is highly imbalanced. Efficient AUC optimization is a challenging research …
Fast, Better Training Trick--Random Gradient
J Wei - arXiv preprint arXiv:1808.04293, 2018 - arxiv.org
In this paper, we will show an unprecedented method to accelerate training and improve
performance, which called random gradient (RG). This method can be easier to the training …
performance, which called random gradient (RG). This method can be easier to the training …
[PDF][PDF] Exploring Machine Learning for Particle Physics
In this article, we report 2 our work on the Kaggle Challenge: Flavours of Physics: Finding
τ−→ µ+ µ− µ−. The main goal of this challenge is to develop powerful classifiers for the …
τ−→ µ+ µ− µ−. The main goal of this challenge is to develop powerful classifiers for the …
Sparse Stochastic Online AUC Optimization for Imbalanced Streaming Data
M Yang, X Cai, R Hu, L Ye, R Zhu - Pacific Rim Conference on Multimedia, 2017 - Springer
Abstract Area Under the ROC Curve (AUC) is an objective indicator of evaluating
classification performance for imbalanced data. In order to deal with large-scale imbalanced …
classification performance for imbalanced data. In order to deal with large-scale imbalanced …