FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images
Human-Computer Interaction
2023-10-05 v1
Abstract
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.
Cite
@article{arxiv.1908.00671,
title = {FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images},
author = {Jieqiong Zhao and Morteza Karimzadeh and Ali Masjedi and Taojun Wang and Xiwen Zhang and Melba M. Crawford and David S. Ebert},
journal= {arXiv preprint arXiv:1908.00671},
year = {2023}
}
Comments
To appear in IEEE VIS 2019 Short Papers