Stacking Label Features for Learning Multilabel Rules

Dependencies between the labels is commonly regarded as the crucial issue in multilabel classification. Rules provide a natural way for symbolically describing such relationships, for instance, rules with label tests in the body allow for representing directed dependencies like implications, subsumptions, or exclusions. Moreover, rules naturally allow to jointly capture both local and global label dependencies.

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

[2]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[3]  Andrew McCallum,et al.  Collective multi-label classification , 2005, CIKM '05.

[4]  Sebastián Ventura,et al.  Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study , 2010, HAIS.

[5]  Pericles A. Mitkas,et al.  Effective Rule-Based Multi-label Classification with Learning Classifier Systems , 2013, ICANNGA.

[6]  Eyke Hüllermeier,et al.  Dependent binary relevance models for multi-label classification , 2014, Pattern Recognit..

[7]  Yihong Gong,et al.  Multi-labelled classification using maximum entropy method , 2005, SIGIR '05.

[8]  Yuhong Guo,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Multi-Label Classification Using Conditional Dependency Networks , 2022 .

[9]  Donato Malerba,et al.  A Multistrategy Approach to Learning Multiple Dependent Concepts , 1996 .

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

[11]  Jan Wessnitzer,et al.  A Model of Non-elemental Associative Learning in the Mushroom Body Neuropil of the Insect Brain , 2007, ICANNGA.

[12]  JOHANNES FÜRNKRANZ,et al.  Separate-and-Conquer Rule Learning , 1999, Artificial Intelligence Review.

[13]  Eneldo Loza Mencía,et al.  Towards Multilabel Rule Learning , 2013, LWA.

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

[15]  Eyke Hüllermeier,et al.  On label dependence and loss minimization in multi-label classification , 2012, Machine Learning.

[16]  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).

[17]  Bo Li,et al.  Multi-label Classification based on Association Rules with Application to Scene Classification , 2008, 2008 The 9th International Conference for Young Computer Scientists.