Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



Although machine learning algorithms are progressively used in an expansive range of domains, the effective machine learning classifiers are often black-boxed, non-comprehensive to the end users and beyond their abilities to develop models themselves. To overcome this challenge, data visualization combined with self-service or democratized machine learning is proposed in the form of the Iterative Logical Classifier (ILC) algorithm with an added advantage of outperforming the accuracies of black-box machine learning classifiers on benchmark datasets. The algorithm is based on the concept of Shifted Paired Coordinates that allow 2-D visualization of n-D data without loss of n-D information.


This article was originally published in 2020 24th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.

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2020 24th International Conference Information Visualisation (IV)


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