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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 Problem Transformation Methods: a Case Study
(Centro Latinoamericano de Estudios en Informática, 2011)
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 ...
An extensive evaluation of decision tree-based hierarchical multilabel classification methods and performance measures
(Wiley-BlackwellMalden, 2015-02)
Hierarchical multilabel classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and ...
Hierarchical multi-label classification using local neural networks
(ElsevierAcademic PressSan Diego, 2014-02)
Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each ...
A new multi-label dataset for Web attacks CAPEC classification using machine learning techniques
Context: There are many datasets for training and evaluating models to detect web attacks, labeling each request as normal or attack. Web attack protection tools must provide additional information on the type of attack ...
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 ...
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. ...
Confidence factor and feature selection for semi-supervised multi-label classification methodsFator de confid?ncia em sele??o de caracter?sticas para m?todos de classifica??o semi-supervisionado multi-r?tulo
(Instituto Federal de Educa??o, Ci?ncia e Tecnologia do Rio Grande do NorteBrasilParnamirimIFRN, 2017)
An empirical comparison of feature selection methods in problem transformation multi-label classification
(Institute of Electrical and Electronics Engineers, 2016-08)
Multi-label classification (MLC) is a supervised learning problem in which a particular example can be associated with a set of labels instead of a single one as in traditional classification. Many real-world applications, ...