Discovering Interpretable Machine Learning Models in Parallel Coordinates
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-5-2021
Abstract
This paper contributes to interpretable machine learning via visual knowledge discovery in parallel coordinates. The concepts of hypercubes and hyper-blocks are used as easily understandable by end-users in the visual form in parallel coordinates. The Hyper algorithm for classification with mixed and pure hyper-blocks (HBs) is proposed to discover hyper-blocks interactively and automatically in individual, multiple, overlapping, and non-overlapping setting. The combination of hyper-blocks with linguistic description of visual patterns is presented too. It is shown that Hyper models generalize decision trees. The Hyper algorithm was tested on the benchmark data from UCI ML repository. It allowed discovering pure and mixed HBs with all data and then with 10-fold cross validation. The links between hyper-blocks, dimension reduction and visualization are established. Major benefits of hyper-block technology and the Hyper algorithm are in their ability to discover and observe hyperblocks by end-users including side by side visualizations making patterns visible for all classes. Another advantage of sets of HBs relative to the decision trees is the ability to avoid both data overgeneralization and overfitting.
Recommended Citation
Kovalerchuk, B., & Hayes, D. (2021). Discovering Interpretable Machine Learning Models in Parallel Coordinates. 2021 25th International Conference Information Visualisation (IV). https://doi.org/10.1109/iv53921.2021.00037
Journal
2021 25th International Conference Information Visualisation (IV)
Rights
Copyright © 2021, IEEE
Comments
This article was originally published in 2021 25th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.
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