BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Multiple labels associative classification
Knowledge and Information Systems
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Aggregating productivity indices for ranking researchers across multiple areas
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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Most current work on classification has been focused on learning from a set of instances that are associated with a single label (i.e., single-label classification). However, many applications, such as gene functional prediction and text categorization, may allow the instances to be associated with multiple labels simultaneously. Multi-label classification is a generalization of single-label classification, and its generality makes it much more difficult to solve.Despite its importance, research on multi-label classification is still lacking. Common approaches simply learn independent binary classifiers for each label, and do not exploit dependencies among labels. Also, several small disjuncts may appear due to the possibly large number of label combinations, and neglecting these small disjuncts may degrade classification accuracy. In this paper we propose a multi-label lazy associative classifier, which progressively exploits dependencies among labels. Further, since in our lazy strategy the classification model is induced on an instance-based fashion, the proposed approach can provide a better coverage of small disjuncts. Gains of up to 24% are observed when the proposed approach is compared against the state-of-the-art multi-label classifiers.