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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)
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 ...
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 to generate synthetic multi-label datasets
(ElsevierAmsterdam, 2014-02-25)
A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. ...
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 ...
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 ...
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 ...
Particle competition and cooperation for semi-supervised learning with label noise
(Elsevier B.V., 2015)
Succinct Representation of Labeled Graphs
(Springer, 2012-02)
In many applications, the properties of an object being modeled are stored as labels on vertices or edges of a graph. In this paper, we consider succinct representation of labeled graphs. Our main results are the succinct ...