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The CART decision tree for mining data streams
One of the most popular tools for mining data streams are decision trees. In this paper we propose a new algorithm, which is based on the commonly known CART algorithm. The most important task inExpand
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Decision Trees for Mining Data Streams Based on the McDiarmid's Bound
In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splittingExpand
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Decision Trees for Mining Data Streams Based on the Gaussian Approximation
Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is toExpand
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New Splitting Criteria for Decision Trees in Stationary Data Streams
The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding’sExpand
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On ensemble components selection in data streams scenario with reoccurring concept-drift
In this article we consider the problem of data stream classification with recurring concept-drift. The proposed method determines when to add or remove a component from an ensemble. The algorithmExpand
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A novel application of Hoeffding's inequality to decision trees construction for data streams
Decision trees are the commonly applied tools in the task of data stream classification. The most critical point in decision tree construction algorithm is the choice of the splitting attribute. InExpand
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A New Method for Data Stream Mining Based on the Misclassification Error
In this paper, a new method for constructing decision trees for stream data is proposed. First a new splitting criterion based on the misclassification error is derived. A theorem is proven showingExpand
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How to adjust an ensemble size in stream data mining?
In this paper we propose a new approach for designing an ensemble applied to stream data classification. Our approach is supported by two theorems showing how to decide whether a new component shouldExpand
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On Pre-processing Algorithms for Data Stream
Clustering is a one of the most important tasks of data mining. Algorithms like the Fuzzy C-Means and Possibilistic C-Means provide good result both for the static data and data streams. AllExpand
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On the Strong Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Non-stationary Environment
Strong convergence of general regression neural networks is proved assuming non-stationary noise. The network is based on the orthogonal series-type kernel. Simulation results are discussed inExpand
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