Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
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
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 Multiple Classifiers with Dynamic Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
An empirical study of applying ensembles of heterogeneous classifiers on imperfect data
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Improving multilabel classification performance by using ensemble of multi-label classifiers
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
| Hi-index | 0.10 |
Multilabel classification is a challenging research problem in which each instance may belong to more than one class. 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 to use a heterogeneous ensemble of multi-label learners to simultaneously tackle both the sample imbalance and label correlation problems. This is different from the existing work in the sense that we are proposing to combine state-of-the-art multi-label methods by ensemble techniques instead of focusing on ensemble techniques within a multi-label learner. The proposed ensemble approach (EML) is applied to six publicly available multi-label data sets from various domains including computer vision, biology and text 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.