A Survey of Genetic Algorithms for Multi-Label Classification

Gonçalves, Eduardo Corrêa; Freitas, Alex A.; Plastino, Alexandre

doi:10.1109/cec.2018.8477927

Cited by 11 publications

(8 citation statements)

References 52 publications

(102 reference statements)

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“…So, in single-label problems, machine learning algorithms have only one possible output prediction. However, some machine learning problems cannot be treated as a single-label problem [27]. There are cases, such as movie classification, where a movie can be classified as action and fiction simultaneously [28].…”

Section: Multi-label and Single-label Learning Methodsmentioning

confidence: 99%

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

et al. 2021

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Section: Multi-label and Single-label Learning Methodsmentioning

confidence: 99%

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

et al. 2021

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“…Os problemas de classificação multirrótulo costumam ser muito mais desafiadores do que os de classificação monorrótulo. Isto se deve as seguintes razões [Gonçalves et al, 2018]:  As aplicações CMR normalmente precisam lidar com um número enorme de combinações de rótulos. Considerando um problema que envolva q rótulos de classe distintos, o tamanho do espaço de resultados em um problema CMR é 2 q , enquanto em um problema de classificação monorrótulo é de apenas q.…”

Section: Como Construir Um Classificador Multirrótulo?unclassified

“…Nos últimos anos diversas estratégias distintas foram propostas na literatura para a construção de classificadores multirrótulo [Gibaja and Ventura, 2014;Gonçalves et al, 2018;Tsoumakas et al, 2010]. Estes métodos podem ser divididos em duas categorias gerais: Adaptação de Algoritmo (AA) e Transformação do Problema (TP).…”

Section: Técnicas De Classificação Multirrótulounclassified

“…De maneira similar, um grande número de composições do músico Tom Jobim consiste em uma mistura de dois gêneros: "Jazz" e "Bossa Nova". Por esta razão, o problema da categorização de músicas representa um problema de classificação multirrótulo (CMRmulti-label classification) [Gibaja and Ventura, 2015;Gonçalves et al, 2018;Tsoumakas et al, 2010;Zhang and Zhou, 2014], em que os objetos podem ser associados a múltiplos rótulos de classe. Ao longo dos últimos anos, diversas outras aplicações modernas e importantes de CMR surgiram.…”

Section: Introductionunclassified

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Introdução à Classificação Multirrótulo

Gonçalves

2018

Minicursos Da ERSI-RJ 2018 - V Escola Regional De Sistemas De Informação Do Rio De Janeiro

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Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. There are many important and modern applications of MLC, such as text categorization (associating documents to various subjects) and semantic scene classification (categorizing images into concepts).This chapter is intended to those who wish to work with MLC in information systems. We present the basic approaches for the construction and evaluation of multi-label classifiers as well as examples of software libraries that support the development of MLC applications in Java and R.

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“…Many methods are available to construct multilabel classifiers, such as naïve Bayes [13], decision tree [14], the k-nearest neighbors [15], support vector machines (SVMs) [16], instance-based learning [17], artificial neural networks [18], and genetic algorithm-based methods [19]. The naïve Bayes classifier (NBC) is a learning method incorporating supervision and guidance mechanisms and is simple and efficient [20].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm

Yan

2021

Symmetry

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As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.

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A Survey of Genetic Algorithms for Multi-Label Classification

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 11 publications

References 52 publications

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

Introdução à Classificação Multirrótulo

Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm

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