Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942606 | Engineering Applications of Artificial Intelligence | 2017 | 10 Pages |
Abstract
In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn++.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn++.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Joana Costa, Catarina Silva, Mário Antunes, Bernardete Ribeiro,