document: On effective classification of strings with wavelets In recent years , the technological advances in mapping genes have made it increasingly easy to store and use a wide variety of biological data . Such data are usually in the form of very long strings for which it is difficult to determine the most relevant features for a classification task . For example , a typical DNA string may be millions of characters long , and there may be thousands of such strings in a database . In many cases , the classification behavior of the data may be hidden in the compositional behavior of certain segments of the string which can not be easily determined apriori . Another problem which complicates the classification task is that in some cases the classification behavior is reflected in global behavior of the string , whereas in others it is reflected in local patterns . Given the enormous variation in the behavior of the strings over different data sets , it is useful to develop an approach which is sensitive to both the global and local behavior of the strings for the purpose of classification . For this purpose , we will exploit the multi-resolution property of wavelet decomposition in order to create a scheme which can mine classification characteristics at different levels of granularity . The resulting scheme turns out to be very effective in practice on a wide range of problems .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O