Adaptive learning for dynamic environments: A comparative approach. (October 2017)

• Record Type: Journal Article
• Authors: Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, Bernardete
• Is Part Of: Engineering applications of artificial intelligence. Volume 65(2017:May)
• Page Start: 336
• Page End: 345
• Publication Date: 2017-10
• Subjects: Dynamic environments -- Ensembles -- Learn++.NSE -- Twitter; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.08.004
• Languages: English
• ISSNs: 0952-1976

Abstract

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).