Mining features for sequence classification

Classification algorithms are difficult to apply to sequential examples because there is a vast number of potentially useful features for describing each example. Past work on feature selection has focused on searching the space of all subsets of features, which is intractable for large feature sets. We adapt sequence mining techniques to aEi as a preprocessor to select features for standard classification algorithms such as Naive Bayes and Winnow. Our experiments on three different datasets show that the features produced by our algorithm improve classification accuracy by lo-50%,