AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning

TL;DR

AstronomicAL is an interactive dashboard that leverages active learning for efficient data labeling, visualization, and classification, adaptable across disciplines including astronomy, enhancing dataset reliability with minimal data.

Contribution

The paper introduces AstronomicAL, a novel interactive tool integrating active learning with visualization for improved data labeling and classification across various fields.

Findings

Effective in reducing data labeling effort

Supports visualization of multi-source data

Facilitates reproducibility through configuration sharing

Abstract

AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless