A Predictive Visual Analytics Evaluation Approach Based on Adaptive Neuro-Fuzzy Inference System

The evaluation of visual analytics (VA) is a challenging field enabling analysts to get insight into diverse data types and formats. It aims at understanding events described by data and supporting the knowledge discovery process by integrating different data analysis methods. Recently, the evolution of intelligent decision support systems has enabled the inductive and predictive approaches of data analysis to make important decisions faster with a higher level of confidence and lower uncertainty. This paper introduces a new and intelligent evaluation method of VA that understands the users' work as well as the features of their environments including vagueness, uncertainty and ambiguity due to workload. To this end, we apply an adaptive neuro-fuzzy inference system (ANFIS) to get quantitative and qualitative measures and determine the lowest evaluation score with better approximation. By combining fuzzy logic, used to deal with the inaccuracies and uncertainty problems during the evaluation process, and neural network, used to solve the problem of continuous changes in assessment environments with the delivery of adaptive learning content. By using the ANFIS approach that allows accurate prediction of evaluation scores, the proposed method seems more efficient compared to the recent evaluation methodology.