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Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Ammar Shaker;  Eyke Hüllermeier — 2014

International Journal of Applied Mathematics and Computer Science

In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known...

An evolutionary approach to constraint-regularized learning.

Eyke Hüllermeier;  Ingo Renners;  Adolf Grauel — 2004

Mathware and Soft Computing

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to employ fuzzy set-based modeling techniques in order to express such knowledge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn ing process within the regularization framework of inductive...

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