BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
A review of feature selection techniques in bioinformatics
Bioinformatics
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Document Transformation for Multi-label Feature Selection in Text Categorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Feature selection for multi-label classification problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Correlated multi-label feature selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Feature Interaction Maximisation
Pattern Recognition Letters
| Hi-index | 0.10 |
Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems.