discovery of significant rules for classifying cancer diagnosis data methods and results we introduce a new method to discover many diversified and significant rules from high dimensional profiling data we also propose to aggregate the discriminating power of these rules for reliable predictions the discovered rules are found to contain lowranked features these features are found to be sometimes necessary for classifiers to achieve perfect accuracy the use of lowranked but essential features in our method is in contrast to the prevailing use of an adhoc number of only topranked features on a wide range of data sets our method displayed highly competitive accuracy compared to the best performance of other kinds of classification models in addition to accuracy our method also provides comprehensible rules to help elucidate the translation between raw data and useful knowledge