79 citations · 350 across the 23 of their papers we have counts for
6 papers · 1 filter
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset posse…
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
Shuo Wang, Leandro L. Minku, Nitesh Chawla +1
With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Class imbalance happens when the…
A Systematic Study of Online Class Imbalance Learning with Concept Drift
Shuo Wang, Leandro L. Minku, Xin Yao
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewe…
Concept Drift Adaptation by Exploiting Historical Knowledge
Yu Sun, Ke Tang, Zexuan Zhu +1
Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data. Two…
Probabilistic Feature Selection and Classification Vector Machine
Bingbing Jiang, Chang Li, Maarten de Rijke +2
Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predic…
Learning in the Model Space for Fault Diagnosis
Huanhuan Chen, Peter Tino, Xin Yao +1
The emergence of large scaled sensor networks facilitates the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cas…