The Adaptation of Concept Drift

With the rapid development of Internet industry, the learning methods based on data stream have attracted more and more attention owing to their great application value in many industries such as banking, insurance, and telecom industry. In the process of learning from stream data, one of the most significant challenges is how to adapt to the so-called concept drift which means that the data stream distribution changes over time in unpredictable ways. To deal with the problem, some methods have been put forward. However, most of them pay more attention to the whole entity of a given model and overlook the impact of local data on the model. In this article, we propose a novel ensemble algorithm to overcome the problem of ignoring local samples in previous methods, namely, the fit prediction algorithm based on local optimum (FPLO) which uses the information of local data to fit (predict) a concept drift. Furthermore, an adaptive method based on the so-called concept changing rate is proposed to choose those classifiers of suitable sizes to overcome the problem of selecting too much or too little historical drift information in previous methods and to make the prediction more accurate. The experimental results on nine synthetic stream datasets and eight real-world stream datasets which all have the concept drift problem show that our FPLO is able to tackle the problem more effectively in comparison to other state-of-the-art methods

Algorithm · Concept drift · Data mining · Data stream · Data stream mining · Machine learning · Computer Science · Data Stream Mining Techniques · Machine Learning and Data Classification · Network Security and Intrusion Detection · Artificial Intelligence

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