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
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Boosting multi-label hierarchical text categorization
Information Retrieval
An Empirical Study of Lazy Multilabel Classification Algorithms
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
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
Multi-label Text Classification Approach for Sentence Level News Emotion Analysis
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Algorithms for subsetting attribute values with Relief
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
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
Graphical feature selection for multilabel classification tasks
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
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Feature selection is an important task in machine learning, which can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features. Although a large body of research deals with feature selection in single-label data, in which measures have been proposed to filter out irrelevant features, this is not the case for multi-label data. This work proposes multi-label feature selection methods which use the filter approach. To this end, two standard multi-label feature selection approaches, which transform the multi-label data into single-label data, are used. Besides these two problem transformation approaches, we use ReliefF and Information Gain to measure the goodness of features. This gives rise to four multi-label feature selection methods. A thorough experimental evaluation of these methods was carried out on 10 benchmark datasets. Results show that ReliefF is able to select fewer features without diminishing the quality of the classifiers constructed using the features selected.