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Explainable Structuring and Discovery of Relevant Cases for Exploration of High-Dimensional Data

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Abstract

Data described by numerous features create a challenge for domain experts as it is difficult to manipulate, explore and visualize them. With the increased number of features, a phenomenon called "curse of dimensionality" arises: sparsity increases and distance metrics are less relevant as most elements of the dataset become equidistant. The result is a loss of efficiency for traditional machine learning algorithms. Moreover, many state-of-the-art approaches act as black-boxes from a user point of view and are unable to provide explanations for their results. We propose an instance-based method to structure datasets around important elements called exemplars. The similarity measure used by our approach is less sensitive to high-dimensional spaces, and provides both explainable and interpretable results: important properties for decision-making tools such as recommender systems. The described algorithm relies on exemplar theory to provide a data exploration tool suited to the reasoning used by experts of various fields. We apply our method to synthetic as well as real-world datasets and compare the results to recommendations made using a nearest neighbor approach.
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Dates and versions

hal-02088283 , version 1 (02-04-2019)

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  • HAL Id : hal-02088283 , version 1

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Joris Falip, Fréderic Blanchard, Michel Herbin. Explainable Structuring and Discovery of Relevant Cases for Exploration of High-Dimensional Data. Intelligent User Interfaces (IUI) Workshops, 2019, Los Angeles, United States. ⟨hal-02088283⟩

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