Supervised Learning in the Gene Ontology Part I: A Rough Set Framework

2005 ◽  
pp. 69-97 ◽  
Author(s):  
Herman Midelfart
Keyword(s):  
Gene Ontology ◽  
Supervised Learning ◽  
Rough Set

scholarly journals GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Kévin Rue-Albrecht ◽  
Paul A. McGettigan ◽  
Belinda Hernández ◽  
Nicolas C. Nalpas ◽  
David A. Magee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Supervised Learning ◽  
Gene Expression Data ◽  
Expression Data ◽  
Bioconductor Package

scholarly journals Applicability of semi-supervised learning assumptions for gene ontology terms prediction

2016 ◽  
Author(s):  
Jorge Alberto Jaramillo-Garzón ◽  
◽  
César Germán Castellanos-Domínguez ◽  
Alexandre Perera-Lluna ◽  
◽  
...  
Keyword(s):  
Gene Ontology ◽  
Supervised Learning

A Semi-Supervised Learning Algorithm for Data Classification

2015 ◽  
Vol 29 (05) ◽  
pp. 1551007 ◽  
Author(s):  
Cheng-Chien Kuo ◽  
Horng-Lin Shieh
Keyword(s):  
Supervised Learning ◽  
Rough Set ◽  
Learning Algorithm ◽  
Data Classification ◽  
Recognition System ◽  
Unlabeled Data ◽  
Power Cable ◽  
Fuzzy Rough Set ◽  
Data Set ◽  
Defect Type

In this study, a semi-supervised learning algorithm for data classification and defect type recognition system for a 25 kV cross-linked polyethylene (XLPE) underground power cable joint is proposed. The proposed algorithm integrates the fuzzy-rough set and shared nearest neighbors (SNN) method for the assignment of labels to unlabeled data. As such, the data set is divided into two subsets: one is labeled and the other is unlabeled. The SNN is adopted for measuring the similarity between unlabeled data and labeled data subsets. Then, according to the levels of similarity, the fuzzy-rough set algorithm is adopted for assigning labels to the unlabeled data. A defect type recognition system XLPE cable classification problem is proposed in order to test the proposed algorithm. To demonstrate the performance of the proposed method, the proposed algorithm is applied to two well-known data sets. The experimental results show that the proposed algorithm can obtain outstanding levels of performance.

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