A framework for generating data to simulate changing environments

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Security Evaluation of Pattern Classifiers under Attack (2014, methodology): “A few works proposed analytical methods to evaluate the security of learning algorithms or of some classes of decision functions (e.g., linear ones), based on more general, application-independent criteria to model the adversary’s behavior (including PAC learning and game theory) [10], [12], [14],…”
DDD: A New Ensemble Approach for Dealing with Concept Drift (2012, background): “We consider that the term concept refers to the whole distribution of the problem in a certain point in time [6], being characterized by the joint distribution pðx; wÞ, where x represents the input attributes and w represents the classes.”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, background): “We are considering that the term concept refers to the whole distribution of the problem in a certain point in time [25], being characterized by the joint distribution pðx x; wÞ, where x represents the input attributes and w represents the classes.”
Learning in Nonstationary Environments: A Survey (2015, background): “html ❏ Kuncheva’s Concept Drift Generator (Matlab): Framework for generating data streams with concept drift [129].”
A survey on data stream clustering and classification (2015, background): “1 Example of a gradual change from a data source S1 to a data source S2 [73].”