Aggregating independent and dependent models to learn multi-label classifiers

Authors:
Elena Montañés;José Ramón Quevedo;Juan José del Coz
Affiliations:
Artificial Intelligence Center, University of Oviedo at Gijón, Spain;Artificial Intelligence Center, University of Oviedo at Gijón, Spain;Artificial Intelligence Center, University of Oviedo at Gijón, Spain
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Year:
2011

Citing 15
Cited 1

Original Contribution: Stacked generalization

Neural Networks
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
Collective multi-label classification

Proceedings of the 14th ACM international conference on Information and knowledge management
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning

Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Correlative multi-label video annotation

Proceedings of the 15th international conference on Multimedia
Trust Region Newton Method for Logistic Regression

The Journal of Machine Learning Research
Multilabel classification via calibrated label ranking

Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification

ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-label Classification Using Ensembles of Pruned Sets

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Combining instance-based learning and logistic regression for multilabel classification

Machine Learning
Classifier Chains for Multi-label Classification

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Regret analysis for performance metrics in multi-label classification: the case of hamming and subset zero-one loss

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I

Dependent binary relevance models for multi-label classification

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of multi-label classification is to automatically obtain models able to tag objects with the labels that better describe them. Despite it could seem like any other classification task, it is widely known that exploiting the presence of certain correlations between labels helps to improve the classification performance. In other words, object descriptions are usually not enough to induce good models, also label information must be taken into account. This paper presents an aggregated approach that combines two groups of classifiers, one assuming independence between labels, and the other considering fully conditional dependence among them. The framework proposed here can be applied not only for multi-label classification, but also in multi-label ranking tasks. Experiments carried out over several datasets endorse the superiority of our approach with regard to other methods in terms of some evaluation measures, keeping competitiveness in terms of others.