ViewClassifier: Visual Analytics on Performance Analysis for Imbalanced Fatal Accident Data

Gulsum Alicioglu, Bo Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The accurate analysis and modeling of high-dimensional imbalanced fatal accident data including its conflicting and overlapping features are significant in terms of public and transportation safety. Traditional visualization techniques and single-valued performance metrics, such as accuracy, obstruct understanding the behavior of machine learning algorithms for imbalanced fatal accident datasets. Therefore, we propose an interactive visualization tool called ViewClassifier to support the analysis of imbalanced fatal accident datasets as well as their prediction results. ViewClassifier enables users to simulate unseen/missing class features through negative sampling by displaying raw data attribute distributions via parallel coordinates. It also allows users to compare multiclass classifiers at first glance and discover model behavior and possible reasons for misclassified results by filtering and examining at instance-level via customized plots. We presented two case studies to show the usage and efficacy of the proposed visualization tool using real-world traffic accident datasets.

Original languageEnglish (US)
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference, IntelliSys
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages481-501
Number of pages21
ISBN (Print)9783030821982
DOIs
StatePublished - 2022
Event Intelligent Systems Conference, IntelliSys 2021 - Virtual, Online
Duration: Sep 2 2021Sep 3 2021

Publication series

NameLecture Notes in Networks and Systems
Volume296
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference Intelligent Systems Conference, IntelliSys 2021
CityVirtual, Online
Period9/2/219/3/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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