Scalable and accurate online feature selection for big data
Feature selection is important in many big data applications. Two critical challenges closely associate with big data. First, in many big data applications, the dimensionality is extremely high, in millions, and keeps growing. Second, big data applications call for highly scalable feature selection algorithms in an online manner such that each feature can be processed in a sequential scan. We present SAOLA, a Scalable and Accurate OnLine Approach for feature selection in this paper. With a theoretical analysis on bounds of the pairwise correlations between features, SAOLA employs novel pairwise comparison techniques and maintains a parsimonious model over time in an online manner. Furthermore, to deal with upcoming features that arrive by groups, we extend the SAOLA algorithm, and then propose a new group-SAOLA algorithm for online group feature selection. The group-SAOLA algorithm can online maintain a set of feature groups that is sparse at the levels of both groups and individual features simultaneously. An empirical study using a series of benchmark real datasets shows that our 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.
Duke Scholars
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Related Subject Headings
- Artificial Intelligence & Image Processing
- 4606 Distributed computing and systems software
- 4605 Data management and data science
- 4604 Cybersecurity and privacy
- 0806 Information Systems
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4606 Distributed computing and systems software
- 4605 Data management and data science
- 4604 Cybersecurity and privacy
- 0806 Information Systems
- 0801 Artificial Intelligence and Image Processing