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Efficient and automated classification of periodic variable stars is becomingincreasingly important as the scale of astronomical surveys grows. Severalrecent papers have used methods from machine learning and statistics toconstruct classifiers on databases of labeled, multi--epoch sources with theintention of using these classifiers to automatically infer the classes ofunlabeled sources from new surveys. However, the same source observed with twodifferent synoptic surveys will generally yield different derived metrics(features) from the light curve. Since such features are used in classifiers,this survey-dependent mismatch in feature space will typically lead to degradedclassifier performance. In this paper we show how and why feature distributionschange using OGLE and \textit{Hipparcos} light curves. To overcome surveysystematics, we apply a method, \textit{noisification}, which attempts toempirically match distributions of features between the labeled sources used toconstruct the classifier and the unlabeled sources we wish to classify. Resultsfrom simulated and real--world light curves show that noisification cansignificantly improve classifier performance. In a three--class problem usinglight curves from \textit{Hipparcos} and OGLE, noisification reduces theclassifier error rate from 27.0% to 7.0%. We recommend that noisification beused for upcoming surveys such as Gaia and LSST and describe some of thepromises and challenges of applying noisification to these surveys.

Optimizing Automated Classification of Periodic Variable Stars in New Synoptic Surveys