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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)
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 ...
Incorporando correlações entre exemplos para classificação multirrótulo via espaço de classes
(Universidade Federal de São CarlosUFSCarCâmpus São CarlosEngenharia de Computação - EC, 2021-06-22)
Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets ...
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 ...
Árvore de predição semi-supervisionada para predição de localização subcelular de proteínas
(Universidade Federal de São CarlosUFSCarCâmpus São CarlosEngenharia de Computação - EC, 2021-11-19)
Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein’s functions. As there are a lot of proteins that reside at the ...
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. ...
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, ...
Feature selection for multi-label learning
(Association for the Advancement of Artificial Intelligence - AAAIInternational Joint Conferences on Artificial Intelligence - IJCAISociedad Argentina de Informática e Investigación Operativa - SADIOUniversidad de Buenos Aires - UBAUniversidad Nacional del Sur - UNSMinisterio de Ciencia, Tecnología e Innovación ProductivaConsejo Nacional de Investigaciones Científicas y Técnicas – CONICETBuenos Aires, 2015-07)
Feature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In ...
Previsão da quantidade de classes em um problema de classificação hierárquica multirrótulo
(Universidade Tecnológica Federal do ParanáPonta GrossaBrasilDepartamento Acadêmico de InformáticaCiência da ComputaçãoUTFPR, 2016-11-24)
In hierarchical multi-label classification problems, each instance may be associated with one or more labels simultaneously belonging to a hierarchical level subclass or superclass. This classification is performed through ...