Enhanced Decision Maps for Exploring Classification Models

Abstract

High-dimensional data is a key study object for both machine learning (ML) and information visualization. In the field of visualization, dimensionality reduction (DR) methods, also known as projections, are one of the most frequently used classes of techniques for visually exploring large and high-dimensional datasets. In ML, high-dimensional data is generated and processed by classifiers and regressors, which increasingly require visualization for explanation and exploration. This thesis focuses on a recent visualization technique called decision maps. A decision map is a 2D image that visualizes the decision boundaries of a classifier in the data space, and can be used to explain and improve the behavior of ML classifiers. Constructing decision maps essentially involves a DR method and its inverse process (inverse projection). As such, ML techniques can help to create decision maps by providing improved (inverse) projections. We begin with a case study applying decision maps to explain the classification of mineral deposit genesis. Our findings show that decision maps provide extra insights into the mineral classification model, aiding geologists in interpreting the model. However, we also identified gaps in current decision map techniques that present opportunities for improvement. Following this case study, we conducted a comprehensive evaluation of three notable decision map techniques. Our evaluation shows that each technique has unique advantages and disadvantages. Our results can guide users in selecting the most suitable technique for specific tasks. A particularly salient finding of this evaluation was that all tested decision maps exhibit a surface-like behavior when applied to a 3D dataset. We explored the aforementioned surface-like behavior of decision maps across more scenarios. By estimating the intrinsic dimensionality of the maps, we found that existing decision map methods cover only a small portion of the intrinsic dimensionality of high-dimensional data spaces. This finding highlights fundamental limitations in all current approaches to constructing decision maps. To address these limitations, we propose a novel approach for computing inverse projections with the support of ML. Our method allows users to interactively control the location of the inversely projected points, thus also of visualizations like decision maps, within the high-dimensional space. In this way, users can explore larger parts of the data space, bypassing the practical limitations imposed by the aforementioned surface-like behavior of decision maps. We demonstrate the effectiveness of our approach through its application to a style transfer task. Finally, we introduce an accelerated computation method for decision maps. Our method significantly reduces the computation time for both basic decision maps and enhanced variations thereof such as gradient maps. The acceleration facilitates the further deployment of such visualizations in interactive visual analytics workflows for classifier engineering.