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In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics...
Data preprocessing and reduction have become essential techniques in current knowledge discovery scenarios, dominated by increasingly large datasets. These methods aim at reducing the complexity inherent to real-world datasets, so that they can be easily processed by current data mining solutions. Advantages of such approaches include, among others, a faster and more precise learning process, and...
Most of data stream learning methods assume that a true class of an incoming instance is available right after it has been processed. However, assumption that we have an unlimited access to class labels is unrealistic and is directly connected with a very high labeling cost. This is a driving force behind growing development of methods that require reduced or no access to class labels. Among several...
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