Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2016
Abstract
We investigate online active learning techniques for online classification tasks. Unlike traditional supervised learning approaches, either batch or online learning, which often require to request class labels of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, aiming to maximize classification performance with minimal human labelling effort during the entire online learning task. In this paper, we present a new family of online active learning algorithms called Passive-Aggressive Active (PAA) learning algorithms by adapting the Passive-Aggressive algorithms in online active learning settings. Unlike conventional Perceptron-based approaches that employ only the misclassified instances for updating the model, the proposed PAA learning algorithms not only use the misclassified instances to update the classifier, but also exploit correctly classified examples with low prediction confidence. Specifically, we propose several variants of PAA algorithms to tackle three types of online learning tasks: binary classification, multi-class classification, and cost-sensitive classification. We give the mistake bounds of the proposed algorithms in theory, and conduct extensive experiments to evaluate the empirical performance of our techniques on both standard and large-scale datasets, in which the encouraging results validate the empirical effectiveness of the proposed algorithms
Keywords
Active learning, Cost-sensitive classification, Multi-class classification, Online learning, Passive-aggressive
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Machine Learning
Volume
103
Issue
2
First Page
141
Last Page
183
ISSN
0885-6125
Identifier
10.1007/s10994-016-5555-y
Publisher
Springer Verlag (Germany)
Citation
LU, Jing; ZHAO, Peilin; and HOI, Steven C. H..
Online Passive-Aggressive Active Learning. (2016). Machine Learning. 103, (2), 141-183.
Available at: https://ink.library.smu.edu.sg/sis_research/3172
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10994-016-5555-y