Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44320
Title: Adaptive learning for dynamic environments: A comparative approach
Authors: Costa, Joana 
Silva, Catarina 
Antunes, Mário 
Ribeiro, Bernardete 
Keywords: Dynamic environments; EnsemblesLearn++.NSETwitter
Issue Date: 2017
Serial title, monograph or event: Engineering Applications of Artificial Intelligence
Volume: 65
Abstract: Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. 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).
URI: http://hdl.handle.net/10316/44320
Other Identifiers: 10.1016/j.engappai.2017.08.004
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
07807338.pdf1.5 MBAdobe PDFView/Open
Show full item record

Page view(s) 5

1,085
checked on Sep 18, 2019

Download(s)

59
checked on Sep 18, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.