Machine Learning
Ensemble learning via negative correlation
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
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
Decision trees for hierarchical multi-label classification
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
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
Effective multi-label active learning for text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning
IEEE Transactions on Knowledge and Data Engineering
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On selection of objective functions in multi-objective community detection
Proceedings of the 20th ACM international conference on Information and knowledge management
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Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the generalization ability of such individual learner can be weak. It is well known that ensemble learning can effectively improve the generalization ability of learning systems by constructing multiple base learners and the performance of an ensemble is related to the both accuracy and diversity of base learners. In this paper, we study the problem of multilabel ensemble learning. Specifically, we aim at improving the generalization ability of multi-label learning systems by constructing a group of multilabel base learners which are both accurate and diverse. We propose a novel solution, called EnML, to effectively augment the accuracy as well as the diversity of multi-label base learners. In detail, we design two objective functions to evaluate the accuracy and diversity of multilabel base learners, respectively, and EnML simultaneously optimizes these two objectives with an evolutionary multi-objective optimization method. Experiments on real-world multi-label learning tasks validate the effectiveness of our approach against other well-established methods.