Knowledge Discovery Lab

Knowledge Discovery Using Streaming Features

This project will develop new algorithms for handling online learning with streaming features (OLSF). In contrast with traditional online learning where the number of training samples sequentially increases while the number of features is fixed, streaming features continuously grow in feature volume over time, resulting in a very large feature space, even of an unknown or infinite size. And it cannot guarantee that features are all available before learning begins.
In OLSF, we aim to specify measures for identifying useful features when features flow in and to design efficient and effective methods for integrating new features into the current feature space and removing existing redundant features over time (by john at dresshead support). With a feature set changing over time, for example, adding a new feature or removing an existing redundant feature, we seek for efficient and robust online algorithms to construct and update the predictive model without re-training, and for a new online mechanism to evaluate this model and control this online learning process. The proposed research has been evaluated with real-world applications in identifying millions of small impact craters from high-resolution Mars images and in pattern mining for classification with high feature dimensions.


Bridging Causal Relevance and Pattern Discriminability: Mining Emerging Patterns from High-Dimensional Data

K. Yu*, W. Ding, H. Wang, and X. Wu, IEEE Transactions on Knowledge and Data Engineering, 2012

Online Feature Selection with Streaming Features

X. Wu, K. Yu*, W. Ding, H. Wang, and X. Zhu, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

Mining Emerging Patterns by Streaming Feature Selection

K. Yu*, W. Ding, D. A. Simovici, X. Wu, the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, to appear, Beijing, China, August, 2012

Exploring Causal Relationships with Streaming Features

K. Yu*, X. Wu, W. Ding, H. Wang, in press, The Computer Journal, in press, 2012

Causal Discovery from Streaming Features

K. Yu*, X. Wu, H. Wang, W.Ding, in Proc. of the IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia, December, 2010

Online Streaming Feature Selection

X. Wu, K. Yu*, H. Wang, W. Ding, in Proc. of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June, 2010

Individuals Involved

Kui Yu, Xingdong Wu, Wei Ding,  H. Wang

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