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| Abstract: |
| Many real-world machine learning applications face the challenge of dealing with changing data over time, known as concept drift, and the issue of data indeterminacy, where all the true labels available are unrealistic. This can lead to a decrease in the accuracy of the prediction models. The aim of this study is to introduce a new approach for detecting drift, which is based on neutrosophic set theory. This approach takes into account uncertainty in the prediction model and is able to handle indeterminate information, considering its impact on the model’s performance. The proposed method reads data into windows and calculates a set of values based on the concept of neutrosophic membership. These values are then used in the Neutrosophic Support Vector Machine (N-SVM). To address the issue of indeterminate true label data, the values issued by N-SVM are expressed as entropy and used as input for the ADWIN (Adaptive Windowing) change detector. When a drift is detected, the prediction model is retrained by including only the most recent instances with the original training data set. The proposed method gives promising results in terms of drift detection accuracy compared to the state of existing drift detection methods such as KSWIN, ADWIN, and DWM. |
| Key words: drift detection indeterminate labels uncertainty neutrosophic set theory data stream |
| DOI：10.11916/j.issn.1005-9113.2024032 |
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