Visual Integration of Data and Model Space in Ensemble Learning

TL;DR

This paper presents a visual framework for exploring and understanding ensemble classifier models by integrating data and model spaces, aiding interpretability and model manipulation.

Contribution

It introduces a novel visual integration workflow that enhances exploration and manipulation of ensemble models and their classification outputs.

Findings

Enables effective exploration of ensemble models and their outputs.

Supports manipulation and comparison of different model combinations.

Improves understanding of model contributions and errors.

Abstract

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.