On sequences of different adaptive mechanisms in non-stationary regression problems

Autor:	Bogdan Gabrys, Rashid Bakirov, Damien Fay
Rok vydání:	2015
Předmět:	Coping (psychology) Software deployment Computer science business.industry Mode (statistics) Regression analysis Artificial intelligence Machine learning computer.software_genre business Adaptation (computer science) computer Regression problems
Zdroj:	IJCNN
DOI:	10.1109/ijcnn.2015.7280779
Popis:	Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we investigate and provide a comparative analysis of the effects of using a flexible order of adaptive mechanisms' deployment resulting in varying adaptation sequences. As a vehicle for this comparison, we use an adaptive ensemble method for regression in batch learning mode which employs several adaptive mechanisms to react to the changes in data. Using real world data from the process industry we demonstrate that such flexible deployment of available adaptive methods embedded in a cross-validatory framework can benefit the predictive accuracy over time.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::42a6c453bcc614fdec0e1ec97fbe7731 https://doi.org/10.1109/ijcnn.2015.7280779 Zobrazit plný text záznamu