Corpus ID: 14520406

Feature ranking for multi-label classification using predictive clustering trees

@inproceedings{Kocev2013FeatureRF,
  title={Feature ranking for multi-label classification using predictive clustering trees},
  author={D. Kocev and I. Slavkov and S. Dzeroski},
  year={2013}
}
In this work, we present a feature ranking method for multilabel data. The method is motivated by the the practically relevant multilabel applications, such as semantic annotation of images and videos, functional genomics, music and text categorization etc. We propose a feature ranking method based on random forests. Considering the success of the feature ranking using random forest in the tasks of classification and regression, we extend this method for multi-label classification. We use… CONTINUE READING

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SHOWING 1-10 OF 19 REFERENCES
Feature Selection for Multi-label Classification Problems
  • 72
  • Highly Influential
  • Open Access
Multi-Label Classification: An Overview
  • 1,968
  • Highly Influential
  • Open Access
An extensive experimental comparison of methods for multi-label learning
  • 508
  • Open Access
Decision trees for hierarchical multi-label classification
  • 480
  • Open Access
A Pruned Problem Transformation Method for Multi-label Classification
  • 169
  • Open Access
Tree ensembles for predicting structured outputs
  • 169
  • Open Access
Mining Multi-label Data
  • 1,207
  • Open Access
Random Forests
  • 46,595
  • Highly Influential
  • Open Access