A collective learning approach for semi-supervised data classification
Abstract
Semi-supervised data classification is one of significant field of study in machine learning and data mining since it deals with datasets which consists both a few labeled and many unlabeled data. The researchers have interest in this field because in real life most of the datasets have this feature. In this paper we suggest a collective method for solving semi-supervised data classification problems. Examples in R-1 presented and solved to gain a clear understanding. For comparison between state of art methods, well-known machine learning tool WEKA is used. Experiments are made on real-world datasets provided in UCI dataset repository. Results are shown in tables in terms of testing accuracies by use of ten fold cross validation.
Source
Pamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri DergisiVolume
24Issue
5URI
https://doi.org/10.5505/pajes.2017.44341https://app.trdizin.gov.tr//makale/TXpBMk9EQTJOZz09
https://hdl.handle.net/20.500.12809/1639