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BoostXML: Gradient Boosting for Extreme Multilabel Text Classification With Tail Labels.

Fengzhi Li, Yuan Zuo, Hao Lin

IEEE Transactions on Neural Networks and Learning Systems

|June 26, 2023

Summary

This summary is machine-generated.

BoostXML improves extreme multilabel learning (XML) by focusing on rare tail labels using gradient boosting. This deep learning method enhances prediction accuracy for less frequent categories in text classification.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Machine Learning
Natural Language Processing
Artificial Intelligence

Background:

Extreme multilabel learning (XML) deals with datasets containing millions of labels, often exhibiting a power-law distribution where most labels appear infrequently (tail labels).
Current deep learning methods for XML excel at head labels but neglect tail labels, which are critical for real-world applications.
Tail labels, though rare, represent important information often missed by standard models.

Purpose of the Study:

To introduce BoostXML, a novel deep learning approach for extreme multilabel text classification.
To enhance the performance of XML models specifically on tail labels.
To address the limitations of existing methods in handling imbalanced label distributions.

Main Methods:

BoostXML employs gradient boosting to enhance a deep learning-based XML method.
A key innovation is optimizing residuals from unfitted instances, prioritizing tail labels in each boosting step.
Includes a Corrective Step to prevent text encoder and weak learner mismatch and a Pretraining Step to mitigate bias towards tail labels.

Main Results:

BoostXML demonstrates significant advantages in tail-label prediction compared to state-of-the-art baselines.
Experiments were conducted on five benchmark datasets, validating the method's effectiveness.
The proposed method shows improved performance by effectively addressing the challenge of imbalanced label distributions.

Conclusions:

BoostXML offers a powerful solution for extreme multilabel text classification, particularly excelling in predicting rare tail labels.
The integration of gradient boosting with deep learning, along with specific optimization steps, effectively tackles the challenge of imbalanced label distributions in XML.
This approach significantly improves the practical utility of XML models in real-world scenarios where tail labels are crucial.