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Visualization for Global Interpretation of Activity Based Classification of Smartphone Users using Random Forest

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2022, 18(2), pp.153-162
  • DOI : 10.29056/jsav.2022.12.15
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : November 19, 2022
  • Accepted : December 20, 2022
  • Published : December 31, 2022

Kim young in 1

1부산대학교

Accredited

ABSTRACT

The importance of convenient, continuous and energy-efficient user classification technology is increasing in mobile applications, and research on user classification methods with good performance based on various user movements is being actively conducted. However, the user classification techniques currently being studied have a large and complex machine learning model, so it is difficult to interpret and explain the classification result. So research is needed to study how to improve this so that researchers can explain the causes of similar users and dissimilar users in the user classification results. In this paper, we classify users by applying the Random Forest classification model developed by optimizing the hyperparameters, and use SHAP(Shapley addictive explanation) of explainable AI(XAI) to visualize and provide the global interpretation for the classification decision from the results. Through the experiment, the results of summarizing which of the features in the experimental data contributed the most and the results of interpreting instances of correctly and misclassified users were presented. As a result, it was shown that the Random Forest classification model can interpret the reason for the classification based on the result, and suggested that SHAP can be used as an explainable technique for the user classification using user activity sensor data.

Citation status

* References for papers published after 2022 are currently being built.