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
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Multi-label Associative Classification of Medical Documents from MEDLINE
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Multilabel classification via calibrated label ranking
Machine Learning
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
An Empirical Study of Lazy Multilabel Classification Algorithms
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Classification of Multi-labeled Data: A Generative Approach
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Generative Probabilistic Model for Multi-label Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
A shared task involving multi-label classification of clinical free text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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It is desirable to determine minimal effective initial local anesthetic bolus required to provide satisfactory analgesia following surgery. A way to predict potential adverse effects based on the type of anesthetic and initial bolus amount administered would be a significant contribution to presonalized medicine. In this work, we propose new methods for multi-label classification to predict adverse effects in order to help doctors make appropriate treatment decisions. In this endeavor, the Pair-Dependency Multi-Label Bayesian Classifier (PDMLBC) and Complete-Dependency Multi-Label Bayesian Classifier (CDMLBC) models are proposed as classifiers that take into account the impact of features on the dependency between labels. We evaluated the proposed models on 36 patients who had recently received arthroscopic shoulder surgery. The experimental results show that the CDMLBC model outperforms other existing methods in multi-label classification.