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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance.

Yange Sun^1,2, Meng Li¹, Lei Li¹

¹School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

Computational Intelligence and Neuroscience

|August 12, 2021

Summary

This summary is machine-generated.

This study introduces a novel Cost-Sensitive based Data Stream (CSDS) classification method to address both class imbalance and concept drift in data streams simultaneously. CSDS effectively handles these challenges, improving classification performance in complex scenarios.

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Area of Science:

Machine Learning
Data Mining
Artificial Intelligence

Background:

Class imbalance and concept drift are significant challenges in data stream classification.
Existing methods often address these issues separately, leaving their combined treatment underexplored.
The complexity increases when class imbalance is present in data streams with concept drift.

Purpose of the Study:

To introduce a novel Cost-Sensitive based Data Stream (CSDS) classification method.
To simultaneously address both class imbalance and concept drift in data streams.
To improve classification performance in imbalanced and concept-drifting data stream scenarios.

Main Methods:

A cost-sensitive learning strategy integrated with the ReliefF algorithm for data-level imbalance alleviation.
A cost-sensitive weighting schema for enhancing ensemble classification performance.
An embedded change detection mechanism for prompt drift capture and reaction.

Main Results:

Experimental results demonstrate the effectiveness of the CSDS method.
The proposed approach achieves better classification results compared to existing methods.
CSDS successfully handles scenarios with both imbalanced classes and concept drift.

Conclusions:

The CSDS classification method offers a unified solution for imbalanced and concept-drifting data streams.
Integrating cost-sensitive strategies at both data preprocessing and classification stages is effective.
The developed change detection mechanism ensures adaptability to evolving data patterns.