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Related Experiment Videos

Fast adapting ensemble: a new algorithm for mining data streams with concept drift.

Agustín Ortíz Díaz¹, José del Campo-Ávila², Gonzalo Ramos-Jiménez²

¹Department of Computer Science, University of Granma, 85100 Granma, Cuba.

Thescientificworldjournal

|April 17, 2015

Summary

This summary is machine-generated.

This study introduces the Fast Adapting Ensemble (FAE), an algorithm designed for data mining that quickly adapts to changing data concepts, even recurring ones. FAE demonstrates improved accuracy and runtime in handling concept drift in large data streams.

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Area of Science:

Data Mining
Machine Learning

Background:

Handling concept drift in large data streams is a significant challenge.
Recurring concepts (those that disappear and reappear) pose additional difficulties for algorithms.

Purpose of the Study:

To present a novel algorithm, Fast Adapting Ensemble (FAE), for effectively treating concept drift in data streams.
To specifically address the challenge of recurring concepts in machine learning.

Main Methods:

The Fast Adapting Ensemble (FAE) processes data in blocks, enabling rapid adaptation without waiting for complete batches.
FAE integrates a drift detector for abrupt concept drift and maintains inactive classifiers for recurring concepts.

Main Results:

The proposed FAE algorithm shows promising results in terms of accuracy and runtime.
FAE effectively handles various types of concept drifts, including abrupt, gradual, and recurring ones.

Conclusions:

FAE offers a fast and effective solution for concept drift in data mining.
The algorithm's ability to manage recurring concepts makes it suitable for dynamic data environments.