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A dynamic ensemble framework for mining textual streams with class imbalance.

Ge Song¹, Yunming Ye¹

¹Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.

Thescientificworldjournal

|July 2, 2014

Summary

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A new framework called Clustering Forest for Imbalanced stream (CFIM) effectively classifies textual data streams. It addresses class imbalance and concept drift by reusing instances, outperforming existing methods.

Area of Science:

Computer Science
Machine Learning
Data Mining

Background:

Textual stream classification is challenging due to large-scale, high-dimensional, non-stationary, and imbalanced data.
Existing methods struggle with imbalanced textual streams, especially when concept drift occurs.

Purpose of the Study:

To propose a novel ensemble framework, Clustering Forest for Imbalanced stream (CFIM), for textual stream classification.
To effectively handle class imbalance and concept drift in textual data streams.

Main Methods:

CFIM integrates multiple clustering trees (CTs) using ensemble learning.
An adaptive selection method dynamically chooses useful CTs based on stream properties.
Rare-class and misclassified instances are collected and reused from historical data chunks to address imbalance.

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The framework assumes both majority and rare classes can experience concept drift.

Main Results:

CFIM demonstrated superior performance on five real-world imbalanced and non-stationary textual streams.
Experimental results showed CFIM outperformed four state-of-the-art ensemble models.
The adaptive instance reuse strategy proved effective in maintaining classification accuracy.

Conclusions:

CFIM offers a robust solution for textual stream classification in challenging imbalanced and concept-drifting environments.
The proposed method effectively mitigates the negative impacts of class imbalance and concept drift.
CFIM provides a significant advancement over existing ensemble models for textual stream analysis.