.png){kind=icon}
.png){kind=icon}
Return
Decision functions for chain classifiers based on Bayesian networks for multi-label classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2015.06.006.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label classification problems require each instance to be assigned a subset of a defined set of labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of binary classes. In this paper we study the decision boundaries of two widely used approaches for building multi-label classifiers, when Bayesian network-augmented naive Bayes classifiers are used as base models: Binary relevance method and chain classifiers. In particular extending previous single-label results to multi-label chain classifiers, we find polynomial expressions for the multi-valued decision functions associated with these methods. We prove upper boundings on the expressive power of both methods and we prove that chain classifiers provide a more expressive model than the binary relevance method. (C) 2015 Elsevier Inc. All rights reserved.
Keywords:
Bayesian network classifier
Multi-label classification
Expressive power
Chain classifier
Binary relevance
Decision functions
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K