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Recently, data streams classification with concept drifting has drawn increasing attention of scholars in data mining, due to the deficiencies of existing algorithms in accuracy and efficient. In this paper, we propose a framework for handling the problem mentioned above using cluster decision tree. We cluster those data which cannot be classified temporarily into n class, and generate new branches...
In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data to accurately predict future samples. For noisy data sources, most existing works rely on data preprocessing techniques to cleanse noisy samples before...
Tracking concept drifts in data streams has recently become a hot topic in data mining. Most of the existing work is built on a single-window-based mechanism to detect concept drifts. Due to the inherent limitation of the single-window-based mechanism, it is a challenge to handle different types of drifts. Motivated by this, a new classification algorithm based on a double-window mechanism for handling...
Classification on noisy data streams has recently become one of the most important topics in streaming data mining. In this paper, a Classification algorithm for mining Data Streams based on Mixture Models of C4.5 and NB is proposed called CDSMM. In this algorithm, C4.5 is used as the base classifiers, the hypothesis testing method is introduced for the detection of concept drifts, and a Naïve Bayes...
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