Content management for electronic music distribution
Communications of the ACM - Digital rights management
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Aggregate features and ADABOOST for music classification
Machine Learning
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Input-agreement: a new mechanism for collecting data using human computation games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improving multilabel analysis of music titles: a large-scale validation of the correction approach
IEEE Transactions on Audio, Speech, and Language Processing
Exploiting diversity in ensembles: improving the performance on unbalanced datasets
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Obtaining Bipartitions from Score Vectors for Multi-Label Classification
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Improving multilabel classification performance by using ensemble of multi-label classifiers
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Supervised dictionary learning for music genre classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Modeling concept dynamics for large scale music search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Classification accuracy is not enough
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
In the field of Music Information Retrieval (MIR), multi-label genre classification is the problem of assigning one or more genre labels to a music piece. In this work, we propose a set of ensemble techniques, which are specific to the task of multi-label genre classification. Our goal is to enhance classification performance by combining multiple classifiers. In addition, we also investigate some existing ensemble techniques from machine learning. The effectiveness of these techniques is demonstrated through a set of empirical experiments and various related issues are discussed. To the best of our knowledge, there has been limited work on applying ensemble techniques to multi-label genre classification in the literature and we consider the results in this work as our initial efforts toward this end. The significance of our work has two folds: (1) proposing a set of ensemble techniques specific to music genre classification and (2) shedding light on further research along this direction.