Image to text translation by multi-label classification
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Detecting malicious web links and identifying their attack types
WebApps'11 Proceedings of the 2nd USENIX conference on Web application development
Multi-label learning approaches for music instrument recognition
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
On the importance of multi-dimensional information in gender estimation from face images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Multilabel classifiers with a probabilistic thresholding strategy
Pattern Recognition
Automated Tagging for the Retrieval of Software Resources in Grid and Cloud Infrastructures
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Learning tree structure of label dependency for multi-label learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Fast multi-label core vector machine
Pattern Recognition
Feature selection for multi-label classification using multivariate mutual information
Pattern Recognition Letters
Multi-label image annotation based on multi-model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Learning to rank from structures in hierarchical text classification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Expert Systems with Applications: An International Journal
A study on multi-label classification
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Robust gender recognition by exploiting facial attributes dependencies
Pattern Recognition Letters
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
Multi-label learning under feature extraction budgets
Pattern Recognition Letters
| Hi-index | 0.01 |
A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called {\rm RA}k{\rm EL} (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that {\rm RA}k{\rm EL} manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.