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Related Experiment Videos

Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle.

Akinori Fujino1, Naonori Ueda, Kazumi Saito

  • 1NTT Communication Science Laboratories, NTT Corporation, Soraku-Gun, Kyoto, Japan. a.fujino@cslab.kecl.ntt.co.jp

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 16, 2008
PubMed
Summary

This study introduces a hybrid classifier for semi-supervised learning, enhancing text classification accuracy with unlabeled data. The method significantly improves generalization when labeled data is scarce.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Semi-supervised learning leverages both labeled and unlabeled data for model training.
  • Traditional generative and discriminative approaches have limitations in data-scarce scenarios.
  • Effective text classification requires robust methods for handling limited labeled examples.

Purpose of the Study:

  • To propose a novel hybrid classifier for semi-supervised learning in multi-class, single-labeled text classification.
  • To enhance classifier generalization by effectively utilizing abundant unlabeled data.
  • To combine generative and discriminative modeling principles for improved performance.

Main Methods:

  • A hybrid approach combining generative (Naive Bayes) and bias correction models.
  • Utilizing the maximum entropy principle to integrate generative and discriminative components.
  • Training and evaluating the hybrid classifier on four diverse text datasets.

Main Results:

  • Significant improvement in generalization ability using unlabeled samples, especially with limited labeled data.
  • Outperformance of the hybrid approach compared to standalone generative and discriminative methods.
  • Demonstrated robustness even when labeled and unlabeled data distributions differ.

Conclusions:

  • The proposed hybrid classifier effectively enhances semi-supervised text classification performance.
  • Unlabeled data plays a crucial role in improving model generalization in low-resource settings.
  • This approach offers a powerful alternative to existing methods for text classification tasks.