Multiclass Datasets, Their Predictions, and Their Visualization
Wallace Brown and Alexander Morrow with Kevin Winner
Senior, Computer Science
Sophomore, Computer Science
Many datasets contain a wealth of information. For example, a person may be described by their race, age, gender, income, marital status, nationality, level of education, etc. By analyzing this data, we can form educated and accurate predictions about individuals. We can, for instance, determine that a person with a particular race, age, nationality, and income is likely to be a college undergraduate. Our goal is to develop ways to visualize these predictions and the uncertainty associated with the predictions. Displaying data in a scatterplot is a standard means of describing two-dimensional information. However, displaying high-dimensional data (i.e., data that includes many attributes, such as age, race, and income) is significantly more challenging. We present a means of visualizing high-dimensional data sets and the predictive models derived from the data, using existing dimension reduction techniques and novel glyph-based displays.
To learn more about the project, check out an interview with Wallace and Alexander.
Catch Wallace and Alexander's presentation at URCAD this Wednesday, April 25 in the University Center 312 from 1:15 to 1:30 p.m.