Machine Learning with UMAP k-NN-ML classification for Data Processing: A case study on LocURa4Iot dataset

Key Points

• Effective classification achieved using machine learning techniques on the LocURa4Iot dataset, and integration of dimensionality reduction is crucial.
• The analysis reports improved accuracy rates compared to conventional methods, including a notable 15% increase in performance metrics.
• Machine learning methods, specifically UMAP followed by k-nearest neighbors, were employed to enhance data processing efficiency.
• Results indicate promising applications for future IoT data analysis frameworks, emphasizing the need for scalable and efficient data handling.