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Label-specific feature selection and two-level label recovery for multi-label classification with missing labels.

Jianghong Ma1, Tommy W S Chow1

  • 1Department of Electronic Engineering, City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.

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|June 30, 2019
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Summary

This study introduces a novel two-level label recovery mechanism to address incomplete labels in multi-label learning. The approach enhances label imputation and prediction accuracy for improved performance in various applications.

Keywords:
Label recoveryLabel-specific feature selectionMissing labelMulti-label learningTwo-level semantic correlations

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Multi-label learning assigns multiple non-exclusive labels to instances.
  • Incomplete label data significantly degrades performance in label-related applications.
  • Existing methods struggle with the inherent sparsity and noise in real-world multi-label datasets.

Purpose of the Study:

  • To develop an effective label imputation and prediction strategy for multi-label learning with incomplete labels.
  • To enhance the robustness and accuracy of multi-label classification models.
  • To improve the performance of label-related applications by addressing label deficiency.

Main Methods:

  • A two-level label recovery mechanism for training set label imputation.
  • Instance-wise and label-wise semantic relational graphs to capture correlations.
  • A label-specific feature selection mechanism for testing set label prediction.
  • Exploiting local and global feature-label connections for inductive classifier learning.

Main Results:

  • The proposed approach demonstrated significant improvements in label imputation accuracy.
  • The label-specific feature selection mechanism proved robust to missing labels.
  • Experimental results on nine diverse datasets confirmed the effectiveness of the method.
  • The two-level semantic relational graphs effectively captured complex label correlations.

Conclusions:

  • The developed two-level label recovery and feature selection mechanisms effectively address incomplete labels in multi-label learning.
  • The proposed method offers a robust and accurate solution for multi-label classification tasks.
  • This work provides a valuable contribution to improving the performance of label-dependent applications.