A collective learning approach for semi-supervised data classification
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...
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| Format: | Article |
| Language: | English |
| Published: |
Pamukkale University
2018-10-01
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| Series: | Pamukkale University Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/pub/pajes/issue/39683/469466 |
| Summary: | 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 R1 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. |
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| ISSN: | 1300-7009 2147-5881 |