An extensive experimental comparison of methods for multi-label learning

[1]  Dragi Kocev,et al.  Ensembles for Predicting Structured Outputs , 2012, Informatica.

[2]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[5]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[6]  Johannes Fürnkranz,et al.  Efficient voting prediction for pairwise multilabel classification , 2010, ESANN.

[7]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[8]  HüllermeierEyke,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009 .

[9]  Eyke Hüllermeier,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009, Machine Learning.

[10]  Geoff Holmes,et al.  Multi-label Classification Using Ensembles of Pruned Sets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[11]  Saso Dzeroski,et al.  Decision trees for hierarchical multi-label classification , 2008, Machine Learning.

[12]  Grigorios Tsoumakas,et al.  An Empirical Study of Lazy Multilabel Classification Algorithms , 2008, SETN.

[13]  Seungok Lee,et al.  Organization of Maximum Surgical Blood Order Schedule (MSBOS) according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) , 2008 .

[14]  Grigorios Tsoumakas,et al.  Multilabel Text Classification for Automated Tag Suggestion , 2008 .

[15]  Grigorios Tsoumakas,et al.  Effective and Efficient Multilabel Classification in Domains with Large Number of Labels , 2008 .

[16]  Krzysztof Trojanowski,et al.  Intelligent Information Processing and Web Mining , 2008 .

[17]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.

[18]  Jesse Read,et al.  A Pruned Problem Transformation Method for Multi-label Classification , 2008 .

[19]  Grigorios Tsoumakas,et al.  Random k -Labelsets: An Ensemble Method for Multilabel Classification , 2007, ECML.

[20]  Johannes Fürnkranz,et al.  Efficient Pairwise Classification , 2007, ECML.

[21]  Saso Dzeroski,et al.  Ensembles of Multi-Objective Decision Trees , 2007, ECML.

[22]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[23]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[24]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[25]  Marcel Worring,et al.  The challenge problem for automated detection of 101 semantic concepts in multimedia , 2006, MM '06.

[26]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[27]  Eyke Hüllermeier,et al.  A Unified Model for Multilabel Classification and Ranking , 2006, ECAI.

[28]  Piotr Synak,et al.  Multi-Label Classification of Emotions in Music , 2006, Intelligent Information Systems.

[29]  A.N. Srivastava,et al.  Discovering recurring anomalies in text reports regarding complex space systems , 2005, 2005 IEEE Aerospace Conference.

[30]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[31]  Peter I. Cowling,et al.  MMAC: a new multi-class, multi-label associative classification approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[32]  Yiming Yang,et al.  The Enron Corpus: A New Dataset for Email Classi(cid:12)cation Research , 2004 .

[33]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[34]  Sunita Sarawagi,et al.  Discriminative Methods for Multi-labeled Classification , 2004, PAKDD.

[35]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[36]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[37]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[38]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[39]  Rémi Gilleron,et al.  Learning Multi-label Alternating Decision Trees from Texts and Data , 2003, MLDM.

[40]  Koby Crammer,et al.  A Family of Additive Online Algorithms for Category Ranking , 2003, J. Mach. Learn. Res..

[41]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[42]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[43]  Amanda Clare,et al.  Knowledge Discovery in Multi-label Phenotype Data , 2001, PKDD.

[44]  Jason Weston,et al.  A kernel method for multi-labelled classification , 2001, NIPS.

[45]  Luc De Raedt,et al.  Top-Down Induction of Clustering Trees , 1998, ICML.

[46]  R. Iman,et al.  Approximations of the critical region of the fbietkan statistic , 1980 .

[47]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[48]  E. S. Pearson.  Biometrika tables for statisticians , 1967 .

[49]  M. Friedman.  A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .