In order to be useful and effectively applicable in dynamically evolving environments, machine learning methods have to meet several requirements, including the ability to analyze incoming data in an online, incremental manner, to observe tight time and memory constraints, and to appropriately respond to changes of the data characteristics and underlying distributions. This paper advocates an instance-based learning algorithm for that purpose, both for classification and regression problems. This algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Notably, our method is very flexible and thus able to adapt to an evolving environment quickly, a point of utmost importance in the data stream context. At the same time, the algorithm is relatively robust and thus applicable to streams with different characteristics.
CITATION STYLE
Shaker, A., & Hüllermeier, E. (2012). Instance-based classification and regression on data streams. In Learning in Non-Stationary Environments: Methods and Applications (Vol. 9781441980205, pp. 185–201). Springer New York. https://doi.org/10.1007/978-1-4419-8020-5_8
Mendeley helps you to discover research relevant for your work.