Skip to main content

Scalable and accurate online feature selection for big data

Publication ,  Journal Article
Yu, K; Wu, X; Ding, W; Pei, J
Published in: ACM Transactions on Knowledge Discovery from Data
December 1, 2016

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

Published In

ACM Transactions on Knowledge Discovery from Data

DOI

EISSN

1556-472X

ISSN

1556-4681

Publication Date

December 1, 2016

Volume

11

Issue

2

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

APA
Chicago
ICMJE
MLA
NLM
Yu, K., Wu, X., Ding, W., & Pei, J. (2016). Scalable and accurate online feature selection for big data. ACM Transactions on Knowledge Discovery from Data, 11(2). https://doi.org/10.1145/2976744
Yu, K., X. Wu, W. Ding, and J. Pei. “Scalable and accurate online feature selection for big data.” ACM Transactions on Knowledge Discovery from Data 11, no. 2 (December 1, 2016). https://doi.org/10.1145/2976744.
Yu K, Wu X, Ding W, Pei J. Scalable and accurate online feature selection for big data. ACM Transactions on Knowledge Discovery from Data. 2016 Dec 1;11(2).
Yu, K., et al. “Scalable and accurate online feature selection for big data.” ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 2, Dec. 2016. Scopus, doi:10.1145/2976744.
Yu K, Wu X, Ding W, Pei J. Scalable and accurate online feature selection for big data. ACM Transactions on Knowledge Discovery from Data. 2016 Dec 1;11(2).

Published In

ACM Transactions on Knowledge Discovery from Data

DOI

EISSN

1556-472X

ISSN

1556-4681

Publication Date

December 1, 2016

Volume

11

Issue

2

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