Visual Integration of Data and Model Space in Ensemble Learning

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.

Paper

Talk

Talk given by Bruno Schneider at VDS as part of the IEEE VIS 2017 in Phoenix, AZ, USA
Powerpoint

Video

Copyright © 2017 Dominik Jäckle