Imagem da capa

Fuzzy conditional random fields for temporal data mining

Na minha lista:
Publicado no:Journal of Physics: Conference Series vol. 893, no. 1 (Oct 2017)
Autor principal: Yulita, Intan Nurma
Outros Autores: Atje Setiawan Abdullah
Publicado em:
IOP Publishing
Assuntos:
Data mining
Markov chains
Graph theory
Fuzzy logic
Conditional random fields
Physics
Acesso em linha:Citation/Abstract
Full Text - PDF
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
Resumo:Temporal data mining is one of the interesting problems in computer science and its application has been performed in a wide variety of fields. The difference between the temporal data mining and data mining is the use of variable time. Therefore, the method used must be capable of processing variables of time. Compared with other methods, conditional random field has advantages in the processing variables of time. The method is a directed graph models that has been widely applied for segmenting and labelling sequence data that appears in various domains. In this study, we proposed use of Fuzzy Logic to be applied in Conditional Random Fields to overcome the problems of uncertainty. The experiment is compared Fuzzy Conditional Random Fields, Conditional Random Fields, and Hidden Markov Models. The result showed that accuracy of Fuzzy Conditional Random Fields is the best.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/893/1/012058
Fonte:Advanced Technologies & Aerospace Database