BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
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
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
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in 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
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
PIKM 2011: the 4th ACM workshop for Ph.D. students in information and knowledge management
Proceedings of the 20th ACM international conference on Information and knowledge management
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
| Hi-index | 0.00 |
Multi-label classification refers to the problem that predicts each single instance to be one or more labels in a set of associated labels. It is common in many real-world applications such as text categorization, functional genomics and semantic scene classification. The main challenge for multi-label classification is predicting the labels of a new instance with the exponential number of possible label sets. Previous works mainly pay attention to transforming the multi-label classification to be single-label classification or modifying the existing traditional algorithm. In this paper, a novel algorithm which combines the advantage of the famous KNN and Random Walk algorithm (RW.KNN) is proposed. The KNN based link graph is built with the k-nearest neighbors for each instance. For an unseen instance, a random walk is performed in the link graph. The final probability is computed according to the random walk results. Lastly, a novel algorithm based on minimizing Hamming Loss to select the classification threshold is also proposed in this paper.