Report
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data
| العنوان: | A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data |
|---|---|
| المؤلفون: | Kuo, Yun-Hsin, Fujiwara, Takanori, Chou, Charles C. -K., Chen, Chun-houh, Ma, Kwan-Liu |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial analysis problems, besides high-dimensional features, the temporal and spatial dependencies of air pollution induce the complexity of performing analysis. Machine learning methods, such as dimensionality reduction, can extract and summarize important information of the data to lift the burden of understanding such a complicated environment. In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects. With this methodology, we develop a visual analytic system that supports a flexible analysis workflow, allowing domain experts to freely explore different aspects based on their analysis needs. We demonstrate the capability of our system and analysis workflow supporting a variety of analysis tasks with multiple use cases. Comment: To appear in the Proceedings of IEEE PacificVis 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2202.05413 |
| رقم الانضمام: | edsarx.2202.05413 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |