Regression

Abstract

Although ensembles of classifiers are one of the most popular tools to deal with data streams classification task [1,2,3,4,5], in the literature there is a lack of new approaches to creating ensembles of regression estimators [6, 7]. Most of the latest developments focus on the application of the regression estimators to solve very important real-world problems. In [8] the authors propose to create an ensemble composed of decision trees, gradient boosted trees and random forest to forecast electricity consumptions. The algorithm uses different weights for each component based on its previous performance. As it was shown in [9], the regression can be applied to enhanced prediction of occurrence of the concept-drift. The authors propose an ensemble method which utilizes constrained penalized regression as a combiner to track a drifting concept in a classification setting. The data stream approach to system fault prediction has been examined in [10]. In this paper different data-stream-based linear regression prediction methods have been tested and compared with a newly developed fault detection system. The applied and evaluated data stream mining algorithms were: grid-based classifier, polygon-based method, and one-class support vector machines. The results showed that the linear regression method generally achieved good performance in predicting short-term data.