Journal article
Extensions to Online Feature Selection Using Bagging and Boosting
IEEE transaction on neural networks and learning systems, v 29(9), pp 4504-4509
Sep 2018
PMID: 29028210
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Feature subset selection can be used to sieve through large volumes of data and discover the most informative subset of variables for a particular learning problem. Yet, due to memory and other resource constraints (e.g., CPU availability), many of the state-of-the-art feature subset selection methods cannot be extended to high dimensional data, or data sets with an extremely large volume of instances. In this brief, we extend online feature selection (OFS), a recently introduced approach that uses partial feature information, by developing an ensemble of online linear models to make predictions. The OFS approach employs a linear model as the base classifier, which allows the l_{0} -norm of the parameter vector to be constrained to perform feature selection leading to sparse linear models. We demonstrate that the proposed ensemble model typically yields a smaller error rate than any single linear model, while maintaining the same level of sparsity and complexity at the time of testing.
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Details
- Title
- Extensions to Online Feature Selection Using Bagging and Boosting
- Creators
- Gregory Ditzler - University of ArizonaJoseph LaBarck - Rowan UniversityJames Ritchie - Rowan UniversityGail Rosen - Drexel UniversityRobi Polikar - Rowan University
- Publication Details
- IEEE transaction on neural networks and learning systems, v 29(9), pp 4504-4509
- Publisher
- IEEE
- Grant note
- Drexel University (10.13039/100008211) 1120622; 1310496 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000443083700048
- Scopus ID
- 2-s2.0-85031778862
- Other Identifier
- 991019169908504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic