Abstract
Understanding the decision boundaries of a machine learning classifier is key to gain insight on how classifiers
work. Recently, a technique called Decision Boundary Map (DBM) was developed to enable the visualization of
such boundaries by leveraging direct and inverse projections. However, DBM have scalability issues for creating
fine-grained maps, and can generate results that are hard to interpret when the classification problem has many
classes. In this paper we propose a new technique called Supervised Decision Boundary Maps (SDBM), which
uses a supervised, GPU-accelerated projection technique that solves the original DBM shortcomings. We show
through several experiments that SDBM generates results that are much easier to interpret when compared to
DBM, is faster and easier to use, while still being generic enough to be used with any type of single-output
classifier
work. Recently, a technique called Decision Boundary Map (DBM) was developed to enable the visualization of
such boundaries by leveraging direct and inverse projections. However, DBM have scalability issues for creating
fine-grained maps, and can generate results that are hard to interpret when the classification problem has many
classes. In this paper we propose a new technique called Supervised Decision Boundary Maps (SDBM), which
uses a supervised, GPU-accelerated projection technique that solves the original DBM shortcomings. We show
through several experiments that SDBM generates results that are much easier to interpret when compared to
DBM, is faster and easier to use, while still being generic enough to be used with any type of single-output
classifier
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) |
| Pages | 77-87 |
| Volume | 3: IVAPP |
| DOIs | |
| Publication status | Published - 2022 |
Keywords
- Machine Learning
- Dimensionality Reduction
- Dense Maps