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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 ...
Interactive Causal Correlation Space Reshape for Multi-Label Classification
Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix ...
Multi-label Problem Transformation Methods: a Case Study
(Centro Latinoamericano de Estudios en Informática, 2011)
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. ...
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 ...
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 ...
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, ...
Meta-aprendizado para análise de desempenho de métodos de classificação multi-label
(Universidade Federal de Pernambuco, 2014)
Lazy multi-label learning algorithms based on mutuality strategies
(SpringerDordrecht, 2015-12)
Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its ...