.png){kind=icon}
Return
Mutual information-based feature selection for multilabel classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2013.06.035.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces a new methodology to perform feature selection in multi-label classification problems. Unlike previous works based on the chi(2) statistics, the proposed approach uses the multivariate mutual information criterion combined with a problem transformation and a pruning strategy. This allows us to consider the possible dependencies between the class labels and between the features during the feature selection process. A way to automatically set the pruning parameter is also proposed, based on the permutation test combined with a resampling strategy. Experiments carried out on both artificial and real-world datasets show the interest of our approach over existing methods. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Feature selection
Mutual information
Multi label classification
Problem transformation
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveMutational landscape of patients with acute myeloid leukemia or myelodysplastic syndromes in the context of RUNX1 mutation.png)
Hematology
OAAI
HematologyIF0
OAAIShareSave
ShareSave
View more