Title
Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
9-7-2020
Abstract
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.
Recommended Citation
Wagle, S. N., & Kovalerchuk, B. (2020). Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning. 2020 24th International Conference Information Visualisation (IV). https://doi.org/10.1109/iv51561.2020.00052
Journal
2020 24th International Conference Information Visualisation (IV)
Rights
Copyright © 2020, IEEE
Comments
This article was originally published in 2020 24th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.