Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Two stage architecture for multi-label learning
Pattern Recognition
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Electronic Notes in Theoretical Computer Science (ENTCS)
Multi-label image annotation based on multi-model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
A Framework to Generate Synthetic Multi-label Datasets
Electronic Notes in Theoretical Computer Science (ENTCS)
Hi-index | 0.00 |
Multilabel classification is a rapidly developing field of machine learning. Despite its short life, various methods for solving the task of multilabel classification have been proposed. In this paper we focus on a subset of these methods that adopt a lazy learning approach and are based on the traditional k-nearest neighbor (k NN) algorithm. Two are our main contributions. Firstly, we implement BRk NN, an adaptation of the k NN algorithm for multilabel classification that is conceptually equivalent to using the popular Binary Relevance problem transformation method in conjunction with the k NN algorithm, but much faster. We also identify two useful extensions of BRk NN that improve its overall predictive performance. Secondly, we compare this method against two other lazy multilabel classification methods, in order to determine the overall best performer. Experiments on different real-world multilabel datasets, using a variety of evaluation metrics, expose the advantages and limitations of each method with respect to specific dataset characteristics.