Treffer: In-depth Analysis and Modeling of Waveform Data: A Comparative Machine Learning Approach

This study provides a comprehensive evaluation of machine learning techniques for waveform data analysis, focusing on preprocessing, feature selection, dimensionality reduction, and modeling. Ensembles like Random Forest and Gradient Boosting demonstrated good performance, while dimensionality reduction techniques, including t-SNE and PCA, offered valuable insights into data structure and class separability. Additionally, methods such as Condensed Nearest Neighbors (CNN) highlighted the importance of optimizing dataset representation for classification tasks. The comparative analysis used metrics like accuracy, and precision, identifying the strengths and limitations of each approach. Future directions include advanced feature engineering, hyperparameter optimization, and leveraging domain-specific insights for improved performance.