VisMCA: A Visual Analytics System for Misclassification Correction and Analysis. VAST Challenge 2020, Mini-Challenge 2 Award: Honorable Mention for Detailed Analysis of Patterns of Misclassification

TL;DR

VisMCA is an interactive visual analytics system designed to help users understand, correct, and analyze misclassifications in machine learning results, especially for object detection, by providing comprehensive views and pattern insights.

Contribution

The paper introduces VisMCA, a novel visual analytics tool that enhances misclassification correction and pattern analysis in ML results, tailored for the VAST Challenge 2020.

Findings

Effective in identifying misclassification patterns

Facilitates accurate re-labeling and correction

Provides comprehensive analytical views

Abstract

This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassification, and providing an analysis of underlying patterns, in response to the VAST Challenge 2020 Mini-Challenge 2. VisMCA facilitates tracking provenance and provides a comprehensive view of object detection results, easing re-labeling, and producing reliable, corrected data for future training. Our solution implements multiple analytical views on visual analysis to offer a deep insight for underlying pattern discovery.