Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on learning with probabilistic representations
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Evaluation of two systems on multi-class multi-label document classification
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Analyzing classification methods in multi-label tasks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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Multilabel classification is an important problem in bioinformatics and Machine Learning. In a conventional classification problem, examples belong to just one among many classes. When an example can simultaneously belong to more than one class, the classification problem is named multilabel classification problem. Protein function classification is a typical example of multilabel classification, since a protein may have more than one function. This paper describes the main characteristics of some multilabel classification methods and applies five methods to protein classification problems. For an experimental comparison of these methods, traditional machine learning techniques are used. The paper also compares different evaluation metrics used in multilabel problems.