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Incorporating label dependency into the binary relevance framework for multi-label classification
(PERGAMON-ELSEVIER SCIENCE LTDOXFORD, 2012)
In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem ...
Multi-label Problem Transformation Methods: a Case Study
(Centro Latinoamericano de Estudios en Informática, 2011)
Label construction for multi-label feature selection
(Universidade de São Paulo - USPUniversidade Federal de São Carlos - UFSCarCentro de Robótica de São Carlos - CROBSociedade Brasileira de Computação - SBCSociedade Brasileira de Automática - SBASão Carlos, 2014-10)
Multi-label learning handles datasets where each instance is associated with multiple labels, which are often correlated. As other machine learning tasks, multi-label learning also suffers from the curse of dimensionality, ...
Labeling association rule clustering through a genetic algorithm approach
(2014-01-01)
Among the post-processing association rule approaches, a promising one is clustering. When an association rule set is clustered, the user is provided with an improved presentation of the mined patterns, since he can have ...
Multi-label semi-supervised classification through optimum-path forest
(Elsevier B.V., 2018-10-01)
Multi-label classification consists of assigning one or multiple classes to each sample in a given dataset. However, the project of a multi-label classifier is usually limited to a small number of supervised samples as ...
Visual active learning for labeling: A case for soundscape ecology data
(2021-07-01)
Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored ...
Particle competition and cooperation for semi-supervised learning with label noise
(Elsevier B.V., 2015-07-21)
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may easily ...
A framework for multi-label exploratory data analysis: ML-EDA
(Universidad de la RepúblicaUniversidad Católica del UruguayUniversidad ORT UruguayUniversidad de MontevideoUniversidad de la EmpresaMontevideo, 2014-09)
Most supervised learning methods consider that each dataset instance is associated with a unique label. However, there are several domains in which the instances are associated with a set of labels (a multi-label). An ...
Evaluating loss minimization in multi-label classification via stochastic simulation using beta distribution
(Universidade Federal do Espírito SantoBRPrograma de Pós-Graduação em InformáticaUFESMestrado em Informática, 2016-05-20)
The objective of this work is to present the effectiveness and efficiency of algorithms for solving the loss minimization problem in Multi-Label Classification (MLC). We first prove that a specific case of loss minimization ...