Adaptive resonance associative map
Neural Networks
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Bloomy Decision Tree for Multi-objective Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
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
Decision trees for hierarchical multi-label classification
Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
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
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Multi-label classification and extracting predicted class hierarchies
Pattern Recognition
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Multi-label classification is an active and rapidly developing research area of data analysis. It becomes increasingly important in such fields as gene function prediction, text classification or web mining. This task corresponds to classification of instances labeled by multiple classes rather than just one. Traditionally, it was solved by learning independent binary classifiers for each class and combining their outputs to obtain multi-label predictions. Alternatively, a classifier can be directly trained to predict a label set of an unknown size for each unseen instance. Recently, several direct multi-label machine learning algorithms have been proposed. This paper presents a novel approach based on ART (Adaptive Resonance Theory) neural networks. The Fuzzy ARTMAP and ARAM algorithms were modified in order to improve their multi-label classification performance and were evaluated on benchmark datasets. Comparison of experimental results with the results of other multi-label classifiers shows the effectiveness of the proposed approach.