Using visual analytics to develop human and machine-centric models: a review of approaches and proposed information technology

Abstract

The use of a visual analytical system in machine learning is the basis for the integration of human and the use of his intellectual capabilities in the construction of models. At the same time, visual analytics is used to expand human knowledge and is used as a research tool. We investigate the forms and goals of using visual analytics workflow towards the formation of the final product. Workflow is divided into human-oriented and machine-oriented in order to build a model as an information processor and decision-
making mechanism. Models are built on the basis of the end user, which can be either a machine or a human. The concepts of model building and the role of machines and humans in these processes are investigated. A practical implementation of the classification information technology in the studied concept ‘using opposite model’ in the machine-oriented visual analytics workflow for using the machine model is proposed.
The basis for this model is a model formed and used by human. To classify data, human intellectual abilities are used. The boundaries of classes are determined by a human and then projected into a hyperspace of attributes with the formation of a classification model that the machine uses. Information technology allows the machine to use a model built for humans.