Abstract
During constructing data stream algorithms the following three aspects have to be taken into consideration: accuracy, running time and required memory. However, in many cases, the fastest algorithms are less accurate than methods requiring high computational power and more time for data analysis. Therefore, to enhance the performance of the algorithms, which in data stream scenario must be characterized by low memory requirement and short time of learning, one can use an ensemble approach. Roughly speaking, the decision made by the ensemble of algorithms can be seen as a decision based on an opinion of a few specialists. In real life nobody is infallible, so to improve the decision making process people often take a final decision after consulting with a few various persons. The vivid example is the diagnosis of an illness. When someone gets bad news, he often goes to other doctors for a second, third, fourth opinion and so on, until we are sure about the diagnosis.
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Rutkowski, L., Jaworski, M., Duda, P. (2020). The General Procedure of Ensembles Construction in Data Stream Scenarios. In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_12
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