Title: Unsupervised Virtual Drift Detection Method in Streaming Environment
Abstract: Real-time applications generate an enormous amount of data that can potentially change data distribution. The underline change in data distribution concerning time causes concept drift. The learning model of the data stream encounters concept drift problems while predicting the patterns. It leads to deterioration in the learning model's performance. Additional challenges of high-dimensional data create memory and time requirements. The proposed work develops an unsupervised concept drift detection method to detect virtual drift in non-stationary data. The K-means clustering algorithm is applied to the relevant features to find the stream's virtual drift. The proposed work reduces the complexity by detecting the drifts using the k highest score features suitable with high-dimensional data. Here, we analyze the data stream's virtual drift by considering the changes in data distribution of recent and current window data instances.
Publication Year: 2023
Publication Date: 2023-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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