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Entropy-based dynamic ensemble classication algorithm for imbalanced data stream with concept drift.

JiaMing Gong^1,2, MingGang Dong³

¹College of Data Science, Guangzhou Huashang College, Guangzhou, Guangdong, China.

|December 13, 2024

Summary

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This study introduces an entropy-based dynamic ensemble classification algorithm (EDAC) to tackle simultaneous class imbalance and concept drift in data streams. EDAC effectively handles imbalanced data and evolving concepts, outperforming existing methods.

Area of Science:

Machine Learning
Data Mining
Artificial Intelligence

Background:

Online learning faces challenges with imbalanced data and concept drift.
Few methods simultaneously address both class imbalance and concept drift in data streams.
Existing approaches often struggle with evolving data distributions.

Purpose of the Study:

To propose a novel algorithm, the entropy-based dynamic ensemble classification (EDAC), for online learning with simultaneous class imbalance and concept drift.
To develop strategies for balancing imbalanced data chunks and adapting to changing data patterns.
To enhance classification accuracy for minority classes in dynamic environments.

Main Methods:

An entropy-based balanced strategy divides data chunks into balanced sample pairs based on information entropy differences.

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A density-based sampling method classifies minority samples into high-quality and common types for improved training.

An ensemble classifier with a self-feedback strategy dynamically adjusts sub-classifier weights to address concept drift.

Main Results:

The proposed EDAC algorithm demonstrated superior performance compared to five state-of-the-art algorithms.
Experiments were conducted on four synthetic and one real-world data stream.
EDAC effectively managed both class imbalance and concept drift in the tested data streams.

Conclusions:

EDAC offers a robust solution for online learning problems characterized by both class imbalance and concept drift.
The combination of entropy-based balancing, density-based sampling, and dynamic ensemble weighting proves effective.
The algorithm shows significant potential for real-world applications dealing with evolving and imbalanced data.