Bibliographic Details
| Main Author: |
Duarte, J |
| Publication Date: |
2014 |
| Other Authors: |
João Gama |
| Format: |
Book
|
| Language: |
eng |
| Source: |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Download full: |
https://hdl.handle.net/10216/83025
|
Summary: |
The volume and velocity of data is increasing at astonishing rates. In order to extract knowledge from this huge amount of information there is a need for efficient on-line learning algorithms. Rule-based algorithms produce models that are easy to understand and can be used almost offhand. Ensemble methods combine several predicting models to improve the quality of prediction. In this paper, a new on-line ensemble method that combines a set of rule-based models is proposed to solve regression problems from data streams. Experimental results using synthetic and real time-evolving data streams show the proposed method significantly improves the performance of the single rule-based learner, and outperforms two state-of-the-art regression algorithms for data streams. |