Back to Search
Start Over
Semi-Supervised k-Star (SSS): A Machine Learning Method with a Novel Holo-Training Approach.
- Source :
- Entropy; Jan2023, Vol. 25 Issue 1, p149, 15p
- Publication Year :
- 2023
-
Abstract
- As one of the entropy-based methods, the k-Star algorithm benefits from information theory in computing the distances between data instances during the classification task. k-Star is a machine learning method with a high classification performance and strong generalization ability. Nevertheless, as a standard supervised learning method, it performs learning only from labeled data. This paper proposes an improved method, called Semi-Supervised k-Star (SSS), which makes efficient predictions by considering unlabeled data in addition to labeled data. Moreover, it introduces a novel semi-supervised learning approach, called holo-training, against self-training. It has the advantage of enabling a powerful and robust model of data by combining multiple classifiers and using an entropy measure. The results of extensive experimental studies showed that the proposed holo-training approach outperformed the self-training approach on 13 out of the 18 datasets. Furthermore, the proposed SSS method achieved higher accuracy (95.25%) than the state-of-the-art semi-supervised methods (90.01%) on average. The significance of the experimental results was validated by using both the Binomial Sign test and the Friedman test. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
SUPERVISED learning
INFORMATION theory
ENTROPY
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 25
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 161480136
- Full Text :
- https://doi.org/10.3390/e25010149