Application of classifier based on STDPM algorithm

Bing Li; Jikui Wang

doi:10.1117/12.2654556

20 October 2022 Application of classifier based on STDPM algorithm

Bing Li, Jikui Wang

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123502Q (2022) https://doi.org/10.1117/12.2654556
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

In the current era of big data, semi-supervised learning algorithms are widely used. The self-training algorithm is one of the commonly used semi-supervised algorithm frameworks. During the iteration process of the self-training algorithm, the classification performance of the base classifier has a significant influence on the final classifier trained by the model. To study the effect of the base classifier on the performance of the STDPM algorithm, we use KNN, decision tree, and SVM base classifier to conduct experiments on four medical images. The experimental results show that on the four data sets diabetes, heart, hepatitis and Ilpd, the three classifiers KNN (K=1), SVM and decision tree have little effect on the STDPM algorithm. When K=3, the algorithm STDPM performs poorly using the KNN base classifier.

Citation Download Citation

Bing Li and Jikui Wang "Application of classifier based on STDPM algorithm", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123502Q (20 October 2022); https://doi.org/10.1117/12.2654556

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