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This payment alternative is attractive to merchants who have dif culties in opening a merchant account with a bank. The merchant account belongs to the master merchant, who is responsible for upholding the agreement with the acquiring bank. The submerchant will sign a separate agreement with the master merchant to authorize the latter to collect on their behalf and to ensure that the master merchant will reimburse the submerchant in return. Among the alternative payment services, these are the leaders and most widely accepted alternative payment methods among e-commerce merchants. Examples of Payment Aggregators AlertPay Amazon Payments Click and Buy eWay eWise Global Collect Google Checkout MercadoLibre Neteller Payment aggregators are the top competitors in the payment landscape and have seen the most widespread adoption. When merchants are talking about alternative payments, they are typically referring to PayPal, Amazon, Bill Me Later, and Google Checkout. Three out of four of these are payment aggregators. The mind share of these companies is also seen by the recent surge in acquisitions as well as the launching of new products. The most popular alternative payment method among the 100 leading retailers surveyed was Bill Me Later, PayPal, and Google Checkout. Only 7 percent of all retailers surveyed offered all three alternative PayDotCom Paymate PayPal Rupay Webfarm Wirecard Valista 2Checkout.com

6 The beauty of mother-of-pearl clouds is indicated by their name. They are occasionally visible in the more highly populated regions of the northern and southern hemisphere, at latitudes above about 50 N. The fascination of these, and other, clouds is entertainingly evoked by Gavin Pretor-Pinney in his Cloudspotter s Guide [32].

with different starting points (beginning with gradual evolution at 0, 50, 100, 150, . . ., 500 online samples), lasting over 50 samples in the drift phase and with two different speeds for gradual forgetting. The results for Operator #2 are shown in Figure 5.14, where the slow gradual forgetting is denoted by the dotted-dashed line and the fast gradual forgetting by the solid line. The sample number in the second half of the training set at which the drift is initialized is represented by the x-axis; the achieved accuracies on separate test data when including the gradual forgetting on the rest of the second half of the training data are represented by the y-axis. In fact, the accuracies for Operator #2 can be boosted up by about 3% (to 91.39% maximum) when initiating a drift phase at the correct sample number and taking the appropriate speed in the gradual forgetting process. In our case, this means that a slow forgetting starting between 100 and 150 and a fast forgetting starting at 350 is bene cial, tracking two different types of drifts. In fact, when combining these two, a classi cation rate of even over 92% can be achieved, gaining another 1% accuracy. Moreover, the class distribution of the CD-imprint data is quite unbalanced as only around 20% of bad samples could be gathered from the production process. In this sense, we applied the timing of update strategy mentioned at the end of section 5.4.2; that is, we updated the fuzzy classi er only with samples from the good class in the case of a disagreement of the classi cation decision and the true class label of the new sample. This gave us another 1.4% more accuracy on the CD-imprint data, compared to the 92% achieved with double drift tracking.

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