FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images
This work addresses feature selection for researchers and practitioners in hyperspectral imaging, but it is incremental as it builds on existing visual analytics methods.
The paper tackles the challenge of feature selection in high-dimensional hyperspectral image regression by introducing FeatureExplorer, a visual analytics system that enables interactive evaluation and refinement of models, resulting in improved predictions and reduced computation time.
Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.