C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Boosting multi-label hierarchical text categorization
Information Retrieval
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
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Algorithms for subsetting attribute values with Relief
Machine Learning
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
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
Correlated multi-label feature selection
Proceedings of the 20th ACM international conference on Information and knowledge management
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In multi-label learning, each example in the dataset is associated with a set of labels, and the task of the generated classifier is to predict the label set of unseen examples. Feature selection is an important task in machine learning, which aims to find a small number of features that describes the dataset as well as, or even better, than the original set of features does. This can be achieved by removing irrelevant and/or redundant features according to some importance criterion. Although effective feature selection methods to support classification for single-label data are abound, this is not the case for multi-label data. This work proposes two multi-label feature selection methods which use the filter approach. This approach evaluates statistics of the data independently of any particular classifier. To this end, ReliefF, a single-label feature selection method and an adaptation of the Information Gain measure for multi-label data are used to find the features that should be selected. Both methods were experimentally evaluated in ten benchmark datasets, taking into account the reduction in the number of features as well as the quality of the generated classifiers, showing promising results.