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Published in:
2017 | OriginalPaper | Chapter
Framework for Exploring and Understanding Multivariate Correlations
Authors : Louis Kirsch, Niklas Riekenbrauck, Daniel Thevessen, Marcus Pappik, Axel Stebner, Julius Kunze, Alexander Meissner, Arvind Kumar Shekar, Emmanuel Müller
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer International Publishing
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Abstract
Feature selection is an essential step to identify relevant and non-redundant features for target class prediction. In this context, the number of feature combinations grows exponentially with the dimension of the feature space. This hinders the user’s understanding of the feature-target relevance and feature-feature redundancy. We propose an interactive Framework for Exploring and Understanding Multivariate Correlations (FEXUM), which embeds these correlations using a force-directed graph. In contrast to existing work, our framework allows the user to explore the correlated feature space and guides in understanding multivariate correlations through interactive visualizations.