Class maps for visualizing classification results

TL;DR

This paper introduces class maps as a visualization tool for classification results, providing insights into label bias, object placement, and data structure across various classifiers and datasets.

Contribution

It proposes a novel visualization method called class maps that reveals probability, distance, and outliers in classification results for multiple algorithms.

Findings

Class maps effectively visualize classification uncertainties and outliers.

Applied to benchmark datasets including images and texts.

Enhances understanding of classifier behavior and data structure.

Abstract

Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. The proposed class map reflects the probability that an object belongs to an alternative class, how far it is from the other objects in its given class, and whether some objects lie far from all classes. The goal is to visualize aspects of the classification results to obtain insight in the data. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and coupling pairwise classifications. It is illustrated on several benchmark datasets, including some about images and texts.