A multi-label approach using binary relevance and decision trees applied to functional genomics

Tanaka, Erica Akemi; Nozawa, Shiro; Macedo, Alessandra Alaniz; Baranauskas, José Augusto

doi:10.1016/j.jbi.2014.12.011

Cited by 37 publications

(18 citation statements)

References 27 publications

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“…While BR has been used in many practical applications, according to [21], it has been widely criticized for its implicit assumption of labels independence which might be completely incorrect in the data. BR+ algorithm [16], [23] is an extension of the BR algorithm where the relationship between labels are considered. The differences are its descriptor attributes, which merge all original attributes as well as all labels, except the label to be predicted.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Classification using an Ontology

Traore¹,

Malo²,

Bassole³

et al. 2019

IJACSA

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During these last few years, the problem of multilabel classification (ML) has been studied in several domains, such as text categorization. Multi-label classification is a main challenging task because each instance can be assigned to multiple classes simultaneously. This paper studies the problem of Multi-label classification in the context of web pages categorization. The categories are defined in an ontology. Among the weakness of the multi-label classification methods, exist the number of positive and negative examples used to build the training dataset of a specific label. So the challenge comes from the huge number of labels combinations that grows exponentially. In this paper, we present an ontology-based Multilabel classification which exploit dependence between the labels. In addition, our approach uses the ontology to take into account relationships between labels and to give the selection of positive and negative examples in the learning phase. In the prediction phase, if a label is not predicted, the ontology is used to prune the set of descendant labels. The results of experimental evaluation show the effectiveness of our approaches.

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Section: Related Workmentioning

confidence: 99%

Multi-Label Classification using an Ontology

Traore¹,

Malo²,

Bassole³

et al. 2019

IJACSA

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“…We prepare a rule set to determine the binary set for each instance based on what kind of gazette that match with particular parsed word. Our model adopts the multi-label classifier to handle context of medical terms used or matched named entity for each instance to construct a dataset of binary record [23]. This because of the execution of Agastya's model determined a sentence with a single label while it possibly contains many of categories that would be classified and beneficial in other aspect of information [3].…”

Section: Integrated Social Media Knowledge Capture Frameworkmentioning

confidence: 99%

“…We see that particular sentence has multiple information in it, then is should be handled in multi-label to maximize of capturing the information. In multi-label classifier, all set of features are able to be assigned as labels [23][24][25]. Therefore, the rule set must have a method to handle what kind of tag that should be marked as a label/predictive label beforehand.…”

Section: Integrated Social Media Knowledge Capture Frameworkmentioning

confidence: 99%

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Integrated Social Media Knowledge Capture in Medical Domain of Indonesia

Surendro¹,

Satya²,

Yodihartomo³

2018

TELKOMNIKA

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The Social Media Platforms, as the one of largest part of today data traffic on the Internet, disseminate a vast volume of information, including medical information in it. Knowledge management system (KMS) approach is applied with purpose to capture, maintain, and manage tacit or explicit knowledge available and collected within the social media platforms, organization's database, knowledge base, or document repository. By adding Indonesian Natural Language Processing (InaNLP), Machine Learning and Data Mining approach, our research has proposed a framework which is theoretically designed to improve the previous research related to social media knowledge capture model and enhance its accuracy and reliability of knowledge retrieved compared to previous knowledge capture model. This system mainly aimed for medical practitioner to give a quick suggestion of the diseases regarding to the early diagnose which has been taken in the first place. On this current research state, the pre-processing phase of the framework implementation and knowledge presentation is our main concernto maximize the information value for the knowledge users and also to reduce the language issues in texts such as ambiguity, inconsistency, use of slang vocabulary, etc.According to this research's goal, we have designed an algorithm to extract feature from dataset.

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“…Após quinze anos de estudo e de contribuições teóricos-práticos a cerca de relacionamento e de manipulação de informação, esta pesquisadora possui a chance de Essa colaboração mútua têm gerado excelentes resultados publicados ao longo dosúltimos sete anos [172,173,13,174,12,68,11,175,176,177,99].…”

Section: Considerações Finaisunclassified

Manipulação e Relacionamento de Informação

Macedo¹

Self Cite

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A Deus pela presença na minha vida. A Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP), a CAPES e ao CNPq, que incentivam, financiam e dão suporteà pesquisa neste país. Aos professores-colegas de profissão, que compartilham a paixão pelo ensino e pela pesquisa e a esperança na transformação do mundo, utilizando essas ferramentas, principalmente ao Prof. Baranauskas com quem tenho colaborado em trabalhos e dividido angústias. Aos pesquisadores-colaboradores do Brasil e do exterior, que apoiam e discutem os trabalhos vislumbrados por mim. Ao pessoal, principalmente aos meus alunos, do laboratório 621 do DCM, que ajudou a traduzir ideias e pensamentos em trabalhos científicos. Aos funcionários e prestadores de serviço do DCM pelo suporte de infraestrutura. A minha família e amigos pelo apoio, carinho e amor eternos. Aos meus sogros e familiares distantes que, mesmo a milhares de quilômetros, sempre me incentivam e enviam muita energia positiva. Ao meu marido, José Antonio, e aos meus filhos, Helena e Santiago, pelo apoio e pela paciência inestimável!! i Sumário Lista de Figuras p. iv Normas e convenções p. vi Resumo p. vii Abstract p. viii 1 Introdução p. 2 Manipulação e Relacionamento de Informação Textual p.

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A multi-label approach using binary relevance and decision trees applied to functional genomics

Cited by 37 publications

References 27 publications

Multi-Label Classification using an Ontology

Multi-Label Classification using an Ontology

Integrated Social Media Knowledge Capture in Medical Domain of Indonesia

Manipulação e Relacionamento de Informação

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