Title
Toward virtual data scientist with visual means
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
7-3-2017
Abstract
The Big data challenge includes dealing with a big number of heterogeneous and multidimensional datasets of all possible sizes not only with data of big size. As a result a huge number of Machine Learning (ML) tasks, which must be solved dramatically exceeds the number of data scientists who can solve these tasks. Next many ML tasks require critical input from subject matter experts (SME) and end users/decision makers who are not ML experts. A set of tools that we call a “virtual data scientist” is needed to assist SMEs and end users to construct ML models for their tasks to meet this Big data challenge with a minimal contribution from data scientists. This paper describes our vision of such a “virtual data scientist” based on the visual approach with collocated and shifted paired coordinates. The approach is illustrated with real world data and ML tasks, as well as simulated data.
Recommended Citation
Kovalerchuk, B., & Kovalerchuk, M. (2017). Toward virtual data scientist with visual means. 2017 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2017.7966238
Journal
2017 International Joint Conference on Neural Networks (IJCNN)
Rights
Copyright © 2017, IEEE
Comments
This article was originally published in 2017 International Joint Conference on Neural Networks (IJCNN). The full-text article from the publisher can be found here.
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