Implicit concept drift detection for multi-label data streams

Date

2022-01

Editor(s)

Advisor

Can, Fazlı

Supervisor

Co-Advisor

Co-Supervisor

Instructor

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Abstract

Many real-world applications adopt multi-label data streams as the need for algo-rithms to deal with rapidly generated data increases. For such streams, changes in data distribution, also known as concept drift, cause the existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an implicit (un-supervised) concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 12 datasets and a baseline classifier. The results show that LD3 provides between 19.8% and 68.6% better predictive performance than comparable detectors on both real-world and synthetic data streams.

Source Title

Publisher

Course

Other identifiers

Book Title

Degree Discipline

Computer Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

Type