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However, since the classi cation decision was in uenced by the preponderance of nonchurners in the data set, we may consider what might happen if we balanced the data set Some data mining algorithms operate best when the relative frequencies of classes in the target variable are not extreme For example, in fraud investigation, such a small percentage of transactions are fraudulent that an algorithm could simply ignore such transactions, classify only nonfraudulent and be correct 9999% of the time Therefore, balanced sampling methods are used to reduce the disparity among the proportions of target classes appearing in the training data For example, in the fraud example, a training data set could be constructed that would contain (1) all of the fraudulent transactions, and (2) only a small percentage, say 1%, of the nonfraudulent transactions.

Many comparative psychologists participate in these The International Society for Comparative psychology, founded in 1983, has great possibilities that have not yet been realized Soul-Searching Comparative psychologists have often assessed the state of their discipline and often criticized the directions taken Three major articles stand out In his famous The Snark Was a Boojum, mentioned earlier, Beach (1950) argued that comparative psychology had begun as the study of a wide range of topics in a wide range of species but had degenerated into the study of learning in rats He stressed the need for a resurrection of the breadth that had earlier characterized the eld Although Beach s analysis was awed (Dewsbury, 1998), it was quite in uential In the second major critique, Hodos and Campbell (1969) criticized comparative psychologists perspective on evolutionary history.

A strati ed sampling technique could then be applied, rst partitioning by fraudulence, then taking all of the fraudulent records and a random sample of 1% of the nonfraudulent records A less desirable approach would be to increase the proportion of fraudulent transactions by cloning them, that is, simply padding the database with copies of existing fraudulent records This amounts to manufacturing data and tends to magnify random attribute values ( Gosh, do 30% of our fraudulent transactions really take place under a full moon ) In our case we have 1449% of the records representing churners, which may be considered somewhat uncommon, although one could argue otherwise Nevertheless, let us balance the training data set so that we have approximately 25% of the records representing churners.

This may be accomplished if we (1) accept all of the churn = true records, and (2) take a random sample of 50% of our churn = false records Since the original data set had 483 churners and 2850 nonchurners, this balancing procedure would provide us with 483/(483 + 1425) = 253% churn = true records, as desired Two drawbacks of balancing the data are that (1) the balanced data set will not have exactly the same character as the original data set, and (2) it is a shame to waste all that data (eg, discard 1425 churn = false records) Because some predictor variables have a higher correlation with the target variable than do other predictor variables, the character of the balanced data will change..

In the case of an image, this may mean reducing the quality of the image (eg, for JPEG) or the number of colors it contains (eg, GIF) Generally, subtle size reductions are unlikely to be noticeable to the receiving user However, more drastic size reductions will degrade the quality of media, and this poses a challenge to determine which method or combination of methods out of the ones listed above will present the receiving user with the best experience of the content Figure 74 illustrates size reduction for JPEG and GIF We see that JPEG le has been reduced to almost a quarter of its already compressed original size through quality reduction The GIF image s le size was reduced by half through color reduction In both cases the quality is still acceptable It is important to note that size reduction strategies are format-dependant.

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