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Related Experiment Videos

Transfer Learning for Class Imbalance Problems with Inadequate Data.

Samir Al-Stouhi1, Chandan K Reddy2

  • 1Honda Research Labs, Dearborn, MI.

Knowledge and Information Systems
|July 6, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel transfer learning method to build robust classifiers for imbalanced datasets with limited training data. The approach augments data and balances classes simultaneously, improving classification accuracy in domains like healthcare.

Keywords:
AdaBoostClass imbalanceHealthCare informaticsRare classText miningTransfer learningWeighted Majority Algorithm

Related Experiment Videos

Area of Science:

  • Data Mining and Machine Learning
  • Computational Statistics
  • Artificial Intelligence

Background:

  • Class imbalance is a significant challenge in data mining, hindering classifier performance.
  • Existing methods often require substantial training data, which is not always available.
  • Developing effective classifiers with limited, imbalanced data is particularly difficult.

Purpose of the Study:

  • To develop a unified framework for building robust classifiers on imbalanced datasets with few training samples.
  • To leverage auxiliary data through transfer learning to augment limited target domain data.
  • To simultaneously address class imbalance and data scarcity.

Main Methods:

  • Proposed a novel boosting-based instance-transfer classifier.
  • Incorporated a label-dependent update mechanism.
  • Optimized for simultaneous data augmentation and class balancing using auxiliary domains.

Main Results:

  • The proposed method effectively compensates for class imbalance.
  • Incorporation of auxiliary domain samples improved classification performance.
  • Theoretical and empirical validation demonstrated the method's robustness and efficacy.

Conclusions:

  • The developed transfer learning framework successfully tackles class imbalance with limited data.
  • The novel boosting-based instance-transfer classifier offers a promising solution for real-world applications.
  • Applied successfully to healthcare and text classification tasks.