Communications of the ACM - Special issue on parallelism
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A new family of online algorithms for category ranking
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multi-label Associative Classification of Medical Documents from MEDLINE
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Hierarchical multi-label prediction of gene function
Bioinformatics
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
The Journal of Machine Learning Research
Semi-automatic dynamic auxiliary-tag-aided image annotation
Pattern Recognition
Multi-label learning by Image-to-Class distance for scene classification and image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Multi-instance multi-label learning
Artificial Intelligence
Categorization of multiple objects in a scene without semantic segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
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Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously, In this learning framework, the inherent ambiguity of each example is explicitly expressed in the output space by being associated with multiple class labels. While on the other hand, its ambiguity is only implicitly encoded in the input space by being represented by only a single instance. Based on this recognition, we hypothesize that if the inherent ambiguity can be explicitly expressed in the input space appropriately, the problem of multi-label learning can be solved more effectively. We justify this hypothesis by proposing a novel multi-label learning approach named INS-DIF. The core of INSDIF is instance differentiation that transforms an example into a bag of instances each of which reflects the example's relationship with one of the possible classes. In this way, INSDIF directly addresses the inherent ambiguity of each example in the input space. A two-level classification strategy is employed to learn from the transformed examples. Applications to automatic web page categorization, natural scene classification and gene functional analysis show that our approach outperforms several well-established multi-label learning algorithms.