Recovery Analysis for Adaptive Learning from Non-stationary Data Streams AmmarShakerandEykeHüllermeier Abstract. The extension of machine learning methods from static to dynamic en- vironments has received increasing attention in recent years; in particular, a large numberofalgorithmsforlearningfromso-called data streams hasbeendeveloped. An important property of dynamic environments is non-stationarity, i.e., the as- sumptionofanunderlyingdatageneratingprocessthatmaychangeovertime.Cor- respondingly,theabilitytoproperlyreacttoso-called concept change isconsidered asanimportantfeatureoflearningalgorithms.Inthispaper,weproposeanewtype ofexperimentalanalysis,called recovery analysis,whichisaimedatassessingthe ability of a learner to discover a concept change quickly, and to take appropriate measurestomaintainthequalityandgeneralizationperformanceofthemodel. 1 Introduction The development of methods for learning from so-called data streams hasbeena topicofactiveresearchinrecentyears[6,9].Roughlyspeaking,thekeyideaisto haveasystemthatlearnsincrementally,andmaybeeveninreal-time,onacontinu- ousandpotentiallyunboundedstreamofdata,andwhichisabletoproperlyadapt itself to changes of environmental conditions or properties of the data generating process. Systems with these properties have already been developed for different machinelearninganddataminingtasks,suchasclusteringandclassification[7]. Anextensionofdataminingandmachinelearningmethodstothesettingofdata streamscomeswithanumberofchallenges.Inparticular,thestandard“batchmode” of learning, in which the entire data as a whole is to provided as an input to the learningalgorithm(or“learner”forshort),isnolongerapplicable.Correspondingly, the learner is not allowed to make several passes through the data set, which is commonly done by standard methods in statistics and machine learning. Instead, AmmarShaker · EykeHüllermeier DepartmentofMathematicsandComputerScience,UniversityofMarburg,Germany e-mail: {shaker,eyke}@mathematik.uni-marburg.de R.Burduketal.(Eds.): CORES 2013,AISC226,pp.289–298. DOI:10.1007/978-3-319-00969-8_ 28 © Springer International Publishing Switzerland 2013