Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed size ensemble of learners running the risk of needlessly spending processing time and memory. In this paper we present improvements to the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for regression that employs social networks theory. In order to detect concept drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show improvements in accuracy, especially in concept drift situations and better performance compared to other state-of-the-art algorithms in both real and synthetic data.
翻译:数据流挖掘是机器学习领域的主要研究之一,由于其在许多领域中的应用。对于数据流挖掘,其中一个主要挑战是概念漂移,需要学习者丢弃当前的概念并适应新概念。集成学习的漂移检测算法已成功应用于分类任务,但通常会保持固定大小的学习器集合,存在浪费处理时间和内存的风险。本文提出了 Scale-free Network Regressor (SFNR) 的改进方法,该方法是基于社交网络理论的动态集成回归算法,使用自适应窗口算法(ADWIN)来检测概念漂移。实验结果表明,在真实数据和合成数据上,在准确性方面取得了改进,尤其是在概念漂移的情况下,与其他最先进的算法相比,SFNR表现更优异。