.png){kind=icon}
Return
ML-FOREST: A Multi-Label Tree Ensemble Method for Multi-Label Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2016.2581161.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called ML-FOREST, to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In ML-FOREST, we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test.
Keywords:
Multi-label classification
label dependency
label transfer
tree classifier
ensemble methods
Journal
IF:
10.4
Papers:
6.9K
Citations:
3.2W
Organization
Cited Papers
ShareSave
ShareSave
ShareSaveAn extensive experimental comparison of methods for multi-label learning.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSave
View more