Online Passive-Aggressive Multilabel Classification Algorithms

Most existing multilabel classification methods are batch learning methods, which may suffer from expensive retraining costs when dealing with new incoming data. In order to overcome the drawbacks of batch learning, we develop a family of online multilabel classification algorithms, which can update...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 12; pp. 10116 - 10129
Main Authors: Zhai, Tingting, Wang, Hao
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Correlation
Data models
Distance learning
Electronic learning
Internet
Labeling
Labels
Learning
Learning systems
Machine learning
Multilabel learning
online learning
online multilabel classification
Optimization
passive-aggressive (PA) learning
Prediction algorithms
Prediction models
Predictive models
Task analysis
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Most existing multilabel classification methods are batch learning methods, which may suffer from expensive retraining costs when dealing with new incoming data. In order to overcome the drawbacks of batch learning, we develop a family of online multilabel classification algorithms, which can update the model instantly and efficiently, and make a timely online prediction when new data arrive. Our algorithms all take a closed-form update, which is obtained by solving a constrained optimization problem in each round of online learning. Label correlation is explicitly modeled in our optimization problem. The label thresholding function, an important component of our online classifier, can also be learned online. Our algorithms can be easily generalized to the nonlinear prediction cases using Mercer kernels. The worst case loss bounds for our algorithms are provided. The bounds are relative to the cumulative loss suffered by the best fixed predictive model that can be attained in hindsight. Finally, we corroborate the merits of our algorithms in both linear and nonlinear predictions on nine open multilabel benchmark datasets.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3164906