Constructing accuracy and diversity ensemble using Pareto-based multi-objective learning for evolving data streams

Ensemble learning is one of the most frequently used techniques for handling concept drift, which is the greatest challenge for learning high-performance models from big evolving data streams. In this paper, a Pareto-based multi-objective optimization technique is introduced to learn high-performanc...

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Bibliographic Details
Published in:Neural computing & applications 2021-06, Vol.33 (11), p.6119-6132
Main Authors: Sun, Yange, Dai, Honghua
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Change detection
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data transmission
Evolution
Image Processing and Computer Vision
Multiple objective analysis
Optimization techniques
Original Article
Pareto optimization
Pareto optimum
Probability and Statistics in Computer Science
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Summary:Ensemble learning is one of the most frequently used techniques for handling concept drift, which is the greatest challenge for learning high-performance models from big evolving data streams. In this paper, a Pareto-based multi-objective optimization technique is introduced to learn high-performance base classifiers. Based on this technique, a multi-objective evolutionary ensemble learning scheme, named Pareto-optimal ensemble for a better accuracy and diversity (PAD), is proposed. The approach aims to enhance the generalization ability of ensemble in evolving data stream environment by balancing the accuracy and diversity of ensemble members. In addition, an adaptive window change detection mechanism is designed for tracking different kinds of drifts constantly. Extensive experiments show that PAD is capable of adapting to dynamic change environments effectively and efficiently in achieving better performance.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05386-5