Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
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
Kernel Discriminant Analysis Using Triangular Kernel for Semantic Scene Classification
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
An Empirical Study of Multi-label Learning Methods for Video Annotation
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Exploiting diversity in ensembles: improving the performance on unbalanced datasets
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Designing a multi-label kernel machine with two-objective optimization
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Enhancing multi-label music genre classification through ensemble techniques
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Classifier selection approaches for multi-label problems
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Multilabel classification using heterogeneous ensemble of multi-label classifiers
Pattern Recognition Letters
Hi-index | 0.00 |
Multilabel classification is a challenging research problem in which each instance is assigned to a subset of labels. Recently, a considerable amount of research has been concerned with the development of “good” multi-label learning methods. Despite the extensive research effort, many scientific challenges posed by e.g. highly imbalanced training sets and correlation among labels remain to be addressed. The aim of this paper is use heterogeneous ensemble of multi-label learners to simultaneously tackle both imbalance and correlation problems. This is different from the existing work in the sense that the later mainly focuses on ensemble techniques within a multi-label learner while we are proposing in this paper to combine these state-of-the-art multi-label methods by ensemble techniques. The proposed ensemble approach (EML) is applied to three publicly available multi-label data sets using several evaluation criteria. We validate the advocated approach experimentally and demonstrate that it yields significant performance gains when compared with state-of-the art multi-label methods.