Comparison of Feature Extraction Methods in High Dimensional Time Series

Working with high-dimensional datasets increases the workload on machine learning models. Therefore, before making predictions, the most meaningful data points in the entire data set must be determined. It is highly important to improve model performance, especially in the field of machine learning. For this reason, five feature selection methods—Mutual Information, Principal Component Analysis, Chi-square, Information Gain, and Variance Thresholding—commonly used in the literature, were tested on the 14400 feature data set obtained with a system previously proposed to determine the sand, silt and clay ratios in the soil. The success of these five methods is presented comparatively using R-square (R²) and Mean Absolute Error (MAE) metrics. The best results were obtained with the Information Gain method for sand (R2 = 0.44), with Chi-square for silt (R2 = 0.17), and with Variance Thresholding for clay (R2 = 0.61).