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Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level

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  • 1Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan.

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Summary

This study introduces a new method for classifying expert-novice levels using a graph convolutional network (GCN) with a confidence-aware attention mechanism. This approach enhances classification accuracy by focusing on significant features and considering the ordinal nature of skill levels.

Keywords:
attention mechanismexpert–novice level classificationgraph convolutional networkmotion data

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Attention mechanisms in classification can highlight non-significant features, reducing accuracy.
  • Classifying expert-novice levels requires nuanced feature interpretation.

Purpose of the Study:

  • To develop an improved classification method for expert-novice levels.
  • To address the limitations of standard attention mechanisms in classification tasks.
  • To leverage the ordinal properties of expert-novice levels for better performance.

Main Methods:

  • Utilized a graph convolutional network (GCN) integrated with a confidence-aware node-level attention mechanism.
  • Incorporated a spatiotemporal attention GCN (STA-GCN) framework.
  • Developed a loss function that accounts for the ordinal nature of expert-novice skill levels.

Main Results:

  • The proposed confidence-aware attention mechanism effectively contrasts node attention values based on classification confidence.
  • The method overcomes the issue of non-significant feature highlighting in attention-based classification.
  • Improved classification performance for expert-novice levels was achieved by considering ordinalities.

Conclusions:

  • The novel confidence-aware node-level attention mechanism enhances classification accuracy in GCNs.
  • Accounting for ordinality in classification models significantly boosts expert-novice level performance.
  • This approach offers a more robust solution for skill-level classification tasks.