Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2012

Abstract

Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features are active and can be used for prediction. We address this challenge by studying sparsity regularization and truncation techniques. Specifically, we present an effective algorithm to solve the problem, give the theoretical analysis, and evaluate the empirical performance of the proposed algorithms for online feature selection on several public datasets. We also demonstrate the application of our online feature selection technique to tackle real-world problems of big data mining, which is significantly more scalable than some well-known batch feature selection algorithms. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques for large-scale applications.

Keywords

Feature Selection, Online Learning, Classification

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

BigMine '12: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications: 12 August 2012, Beijing

First Page

93

Last Page

100

ISBN

9781450315470

Identifier

10.1145/2351316.2351329

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2351316.2351329

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