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—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 splitting attribute. In literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g. information gain or Gini index. In this paper it is(More)
—Since the Hoeffding Tree algorithm was proposed in literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly(More)
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 in constructing decision trees for data streams is to determine the best attribute to make a split in the considered node. To solve this problem we apply the(More)
UNLABELLED Skinfold thicknesses are used as valid anthropometric indicators of regional body fatness. Actual population-based values for skinfold thicknesses for Polish children are not available. The purpose of this study was to provide population-based values for triceps, subscapular, and abdominal skinfold thicknesses in healthy children and adolescents.(More)
INTRODUCTION Unruptured intracranial aneurysms (UIAs) are frequently detected in noninvasive imaging studies such as computed tomography angiography (CTA) or magnetic resonance angiography (MRA). If small, UIAs are observed in these modalities in order to detect growth or shape change, but there are many questions about proper protocol of the follow-up. (More)
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 showing that the best attribute computed in considered node according to the available data sample is the same, with some high probability, as the attribute derived from(More)