Semi-Supervised k-Star (SSS): A Machine Learning Method with a Novel Holo-Training Approach

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ID: 276240
2023
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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 (SSS), which makes efficient predictions by considering unlabeled data in addition to labeled data. Moreover, it introduces a novel semi-supervised learning approach, called , 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.
Reference Key
birant2023semisupervisedentropy Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Birant, K.U.
Journal entropy
Year 2023
DOI
10.3390/e25010149
URL
Keywords Keywords not found

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