Порівняльний аналіз модифікованих алгоритмів навчання з частковим залученням учителя на малій кількості розмічених даних
The paper is devoted to improving semi-supervised clustering methods and comparing their accuracy and robustness. The proposed approach is based on expanding a clustering algorithm for using an available set of labels by replacing the distance function. Using the distance function considers not only...
Збережено в:
| Видавець: | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
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| Дата: | 2022 |
| Автори: | , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2022
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| Теми: | |
| Онлайн доступ: | http://journal.iasa.kpi.ua/article/view/239726 |
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Організація
System research and information technologies| Резюме: | The paper is devoted to improving semi-supervised clustering methods and comparing their accuracy and robustness. The proposed approach is based on expanding a clustering algorithm for using an available set of labels by replacing the distance function. Using the distance function considers not only spatial data but also available labels. Moreover, the proposed distance function could be adopted for working with ordinal variables as labels. An extended approach is also considered, based on a combination of unsupervised k-medoids methods, modified for using only labeled data during the medoids calculation step, supervised method of k nearest neighbor, and unsupervised k-means. The learning algorithm uses information about the nearest points and classes’ centers of mass. The results demonstrate that even a small amount of labeled data allows us to use semi-supervised learning, and proposed modifications improve accuracy and algorithm performance, which was found during experiments. |
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