A Visual Analytics Tool to Explore Multi-Classification Model with High Number of Classes
Pages 84 - 86
Abstract
Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides overview+detail style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.
References
[1]
Bilal Alsallakh, Allan Hanbury, Helwig Hauser, Silvia Miksch, and Andreas Rauber. 2014. Visual Methods for Analyzing Probabilistic Classification Data. IEEE TVCG 20, 12 (2014), 1703–1712. https://doi.org/10.1109/TVCG.2014.2346660
[2]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248–255. https://doi.org/10.1109/CVPR.2009.5206848
[3]
Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu. 2017. Towards Better Analysis of Deep Convolutional Neural Networks. IEEE TVCG 23, 1 (2017), 91–100. https://doi.org/10.1109/TVCG.2016.2598831
[4]
Jose Gustavo S. Paiva, William Robson Schwartz, Helio Pedrini, and Rosane Minghim. 2015. An Approach to Supporting Incremental Visual Data Classification. IIEEE TVCG 21, 1 (2015), 4–17. https://doi.org/10.1109/TVCG.2014.2331979
[5]
Luc-Etienne Pommé, Romain Bourqui, Romain Giot, and David Auber. 2022. Relative Confusion Matrix: Efficient Comparison of Decision Models. In International Conference Information Visualisation (IV). 98–103. https://doi.org/10.1109/IV56949.2022.00025
[6]
Donghao Ren, Saleema Amershi, Bongshin Lee, Jina Suh, and Jason D. Williams. 2017. Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers. IIEEE TVCG 23, 1 (2017), 61–70. https://doi.org/10.1109/TVCG.2016.2598828
[7]
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, 2015. Imagenet large scale visual recognition challenge. Springer IJCV 115 (2015), 211–252. https://doi.org/10.1007/s11263-015-0816-y
[8]
Christin Seifert and Elisabeth Lex. 2009. A Novel Visualization Approach for Data-Mining-Related Classification. In 2009 13th International Conference Information Visualisation. 490–495. https://doi.org/10.1109/IV.2009.45
[9]
Keito Uwaseki, Kazuyuki Fujita, Kazuki Takashima, and Yoshifumi Kitamura. 2022. ConfusionLens: Dynamic and Interactive Visualization for Performance Analysis of Multiclass Image Classifiers. In Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22 Adjunct). Association for Computing Machinery, New York, NY, USA, Article 60, 3 pages. https://doi.org/10.1145/3526114.3558631
Index Terms
- A Visual Analytics Tool to Explore Multi-Classification Model with High Number of Classes
Recommendations
Big data exploration through visual analytics
VAST '12: Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)SAS® Visual Analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS Visual Analytics solution. It excels at handling big data problems like the VAST challenge. With a wide range of ...
Visual Analytics Infrastructures: From Data Management to Exploration
Analysts exploring big data require more from information visualization, data analysis, and data management than these components can now deliver. New infrastructures must address the nature of exploration as well as data scale. The Web extra at http://...
A Visual Analytics Tool for Analysing Microarray Data
ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining WorkshopsThis paper presents a new visual analytics tool for analysing microarray data with several thousands of attributes. The tool includes two components 1) automated data analysis and 2) interactive visualization. Automated data analysis is used to reduce ...
Comments
Information & Contributors
Information
Published In
June 2024
129 pages
ISBN:9798400706516
DOI:10.1145/3660515
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 June 2024
Check for updates
Author Tags
Qualifiers
- Poster
- Research
- Refereed limited
Conference
EICS '24
Sponsor:
EICS '24: The 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
June 24 - 28, 2024
Cagliari, Italy
Acceptance Rates
Overall Acceptance Rate 73 of 299 submissions, 24%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)16
- Downloads (Last 6 weeks)16
Reflects downloads up to 26 Jul 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format