The nature of statistical learning theory
The nature of statistical learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Fuzzy clustering with squared Minkowski distances
Fuzzy Sets and Systems - Special issue on clustering and learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Hierarchical classification: combining Bayes with SVM
ICML '06 Proceedings of the 23rd international conference on Machine learning
ACM Transactions on Information Systems (TOIS)
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Hierarchical multi-label prediction of gene function
Bioinformatics
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Label ranking by learning pairwise preferences
Artificial Intelligence
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
An Empirical Study of Lazy Multilabel Classification Algorithms
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Document Transformation for Multi-label Feature Selection in Text Categorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning to rank with a novel kernel perceptron method
Proceedings of the 18th ACM conference on Information and knowledge management
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Semantic Home Photo Categorization
IEEE Transactions on Circuits and Systems for Video Technology
RW.KNN: a proposed random walk KNN algorithm for multi-label classification
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Instance-Ranking: a new perspective to consider the instance dependency for classification
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
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Multi-label classification refers to learning tasks with each instance belonging to one or more classes simultaneously. It arose from real-world applications such as information retrieval, text categorization and functional genomics. Currently, most of the multi-label learning methods use the strategy called binary relevance, which constructs a classifier for each unique label by grouping data into positives (examples with this label) and negatives (examples without this label). With binary relevance, an example with multiple labels is considered as a positive data for each label it belongs to. For some classes, this data point may behave like an outlier confusing classifiers, especially in the cases of well-separated classes. In this paper, we first introduce a new strategy called soft relevance, where each multi-label example is assigned a relevance score to the labels it belongs to. This soft relevance is then employed in a voting function used in a k nearest neighbor classifier. Furthermore, a voting-margin ratio is introduced to the k nearest neighbor classifier for better performance. We compare the proposed method to other multi-label learning methods over three multi-label datasets and demonstrate that the proposed method provides an effective way to multi-label learning.