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Tomaž Stepišnik1,2, Dragi Kocev1,2,3

  • 1Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.

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|May 14, 2021
PubMed
Summary

Semi-supervised oblique predictive clustering trees (SSL-SPYCTs) offer efficient learning by using linear combinations of features. This method scales linearly with features and often outperforms existing semi-supervised and supervised approaches.

Keywords:
Oblique decision treesPredictive clustering treesSemi-supervised learningStructured output prediction

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Semi-supervised learning leverages both labeled and unlabeled data, crucial for tasks like drug repurposing where labels are scarce.
  • Semi-supervised predictive clustering trees (SSL-PCTs) are effective but suffer from quadratic scaling with feature numbers.
  • Oblique predictive clustering trees (SPYCTs) use feature combinations for more expressive splits, enhancing predictive performance.

Purpose of the Study:

  • Introduce semi-supervised oblique predictive clustering trees (SSL-SPYCTs) to address the computational limitations of SSL-PCTs.
  • Develop an efficient semi-supervised learning method that scales linearly with the number of features.
  • Improve predictive modeling accuracy and feature importance analysis in data-limited scenarios.

Main Methods:

  • Proposed SSL-SPYCTs by adapting split learning to incorporate unlabeled data efficiently.
  • Utilized linear combinations of features for oblique splits, enhancing model expressiveness.
  • Designed a criterion function for efficient optimization of oblique splits.

Main Results:

  • SSL-SPYCTs demonstrate a linear scaling with the number of features, a significant improvement over SSL-PCTs.
  • Experimental results show SSL-SPYCTs often outperform SSL-PCTs and supervised PCTs in both single-tree and ensemble settings.
  • SSL-SPYCTs provide more meaningful feature importance scores than supervised SPYCTs when labeled data is limited.

Conclusions:

  • SSL-SPYCTs offer a computationally efficient and effective approach to semi-supervised learning.
  • The method enhances predictive performance and feature interpretability, especially in low-label data regimes.
  • SSL-SPYCTs represent a valuable advancement for predictive modeling tasks with abundant unlabeled data.