Online Feature Selection with Streaming Features
- Xindong Wu, Kui Yu, W. Ding, Hao Wang, Xingquan Zhu
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 May 2013
A novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly and an efficient Fast-OSFS algorithm is proposed to improve feature selection performance.
Data mining with big data
- Xindong Wu, Xingquan Zhu, Gongqing Wu, W. Ding
- Computer ScienceIEEE Transactions on Knowledge and Data…
- 2014
A HACE theorem is presented that characterizes the features of the Big Data revolution, and a Big Data processing model is proposed, from the data mining perspective, which involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations.
Online Streaming Feature Selection
- Xindong Wu, Kui Yu, Hao Wang, W. Ding
- Computer ScienceInternational Conference on Machine Learning
- 21 June 2010
Under this framework, a promising alternative method, Online Streaming Feature Selection (OSFS), is presented to online select strongly relevant and non-redundant features.
Scalable and Accurate Online Feature Selection for Big Data
- Kui Yu, Xindong Wu, W. Ding, J. Pei
- Computer ScienceACM Transactions on Knowledge Discovery from Data
- 30 November 2015
An empirical study using a series of benchmark real datasets shows that the two algorithms, SAOLA and group-SAOLA, are scalable on datasets of extremely high dimensionality and have superior performance over the state-of-the-art feature selection methods.
Towards Scalable and Accurate Online Feature Selection for Big Data
- Kui Yu, Xindong Wu, W. Ding, J. Pei
- Computer ScienceIEEE International Conference on Data Mining
- 14 December 2014
An empirical study shows that SAOLA is scalable on data sets of extremely high dimensionality, and has superior performance over the state-of-the-art feature selection methods.
Crime Forecasting Using Data Mining Techniques
- Chung-Hsien Yu, Max W. Ward, Melissa Morabito, W. Ding
- Computer ScienceIEEE 11th International Conference on Data Mining…
- 11 December 2011
The preliminary results of a crime forecasting model developed in collaboration with the police department of a United States city in the Northeast are discussed, which takes advantage of implicit and explicit spatial and temporal data to make reliable crime predictions.
Domain agnostic online semantic segmentation for multi-dimensional time series
- Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Eamonn J. Keogh
- Computer ScienceData mining and knowledge discovery
- 25 September 2018
A multi-dimensional algorithm is presented, which is domain agnostic, has only one, easily-determined parameter, and can handle data streaming at a high rate, and is tested on the largest and most diverse collection of time series datasets ever considered.
Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features
- L. Bandeira, W. Ding, T. Stepinski
- Physics
- 2012
Detecting Impact Craters in Planetary Images Using Machine Learning
- T. Stepinski, W. Ding, R. Vilalta
- Geology
- 2012
Prompted by crater counts as the only available tool for measuring remotely the relative ages of geologic formations on planets, advances in remote sensing have produced a very large database of high…
Self-Taught Active Learning from Crowds
- Meng Fang, Xingquan Zhu, Bin Li, W. Ding, Xindong Wu
- Computer ScienceIEEE 12th International Conference on Data Mining
- 10 December 2012
A probabilistic model is employed to characterize the knowledge of each labeler through which a weak labeler can learn complementary knowledge from a stronger peer and eventually helps achieve high classification accuracy with minimized labeling costs and labeling errors.
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