Abstract
Working with a very large feature set is a challenge in the current machine learning research. In this paper, we address the feature-selection problem in the context of training AdaBoost classifiers. The AdaBoost algorithm embeds a feature selection mechanism based on training a classifier for each feature. Learning the single-feature classifiers is the most time consuming part of AdaBoost training, especially when large number of features are available. To solve this problem, we generate a working feature subset using a novel feature subset selection method based on the partial least square regression, and then train and select from this feature subset. The partial least square method is capable of selecting high-dimensional and highly redundant features. The experiments show that the proposed PLS-based feature-selection method generates sensible feature subsets for AdaBoost in a very efficient way.
Original language | English |
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Title of host publication | Electronic Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011 |
DOIs | |
Publication status | Published - 7 Nov 2011 |
Event | 2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011 - Barcelona, Spain Duration: 11 Jul 2011 → 15 Jul 2011 |
Conference
Conference | 2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011 |
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Country/Territory | Spain |
City | Barcelona |
Period | 11/07/11 → 15/07/11 |
Keywords
- AdaBoost
- Feature selection
- Partial Least Squares
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications