Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanagphongs, E. Bertini
{"title":"A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations","authors":"Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanagphongs, E. Bertini","doi":"10.1109/VAST.2017.8585720","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585720","url":null,"abstract":"Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualizing Real-Time Strategy Games: The Example of StarCraft II","authors":"Yen-Ting Kuan, Yu-Shuen Wang, Jung-Hong Chuang","doi":"10.1109/VAST.2017.8585594","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585594","url":null,"abstract":"We present a visualization system for users to examine real-time strategy games, which have become very popular globally in recent years. Unlike previous systems that focus on showing statistics and build order, our system can depict the most important part – battles in the games. Specifically, we visualize detailed movements of armies belonging to respective nations on a map and enable users to examine battles from a global view to a local view. In the global view, battles are depicted by curved arrows revealing how the armies enter and escape from the battlefield. By observing the arrows and the height map, users can make sense of offensive and defensive strategies easily. In the local view, units of each type are rendered on the map, and their movements are represented by animation. We also render an attack line between a pair of units if one of them can attack the other to help users analyze the advantages and disadvantages of a particular formation. Accordingly, users can utilize our system to discover statistics, build order, and battles, and learn strategies from games played by professionals.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}