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A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments - 2019

A Novel Concept Drift Detection Method For Incremental Learning In Nonstationary Environments

Research Paper on A Novel Concept Drift Detection Method For Incremental Learning In Nonstationary Environments

Research Area:  Machine Learning

Abstract:

We present a novel method for concept drift detection, based on: 1) the development and continuous updating of online sequential extreme learning machines (OS-ELMs) and 2) the quantification of how much the updated models are modified by the newly collected data. The proposed method is verified on two synthetic case studies regarding different types of concept drift and is applied to two public real-world data sets and a real problem of predicting energy production from a wind plant. The results show the superiority of the proposed method with respect to alternative state-of-the-art concept drift detection methods. Furthermore, updating the prediction model when the concept drift has been detected is shown to allow improving the overall accuracy of the energy prediction model and, at the same time, minimizing the number of model updatings.

Keywords:  
Drift Detection
Incremental Learning
Machine Learning
Deep Learning

Author(s) Name:  Zhe Yang; Sameer Al-Dahidi; Piero Baraldi; Enrico Zio; Lorenzo Montelatici

Journal name:  IEEE Transactions on Neural Networks and Learning Systems

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TNNLS.2019.2900956

Volume Information:  Volume: 31, Issue: 1, Jan. 2020