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Statistical detection of a rare class of objects in a two-classclassification problem can pose several challenges. Because the class ofinterest is rare in the training data, there is relatively little informationin the known class response labels for model building. At the same time theavailable explanatory variables are often moderately high dimensional. In thefour assays of our drug-discovery application, compounds are active or notagainst a specific biological target, such as lung cancer tumor cells, andactive compounds are rare. Several sets of chemical descriptor variables fromcomputational chemistry are available to classify the active versus inactiveclass; each can have up to thousands of variables characterizing molecularstructure of the compounds. The statistical challenge is to make use of therichness of the explanatory variables in the presence of scant responseinformation. Our algorithm divides the explanatory variables into subsetsadaptively and passes each subset to a base classifier. The various baseclassifiers are then ensembled to produce one model to rank new objects bytheir estimated probabilities of belonging to the rare class of interest. Theessence of the algorithm is to choose the subsets such that variables in thesame group work well together; we call such groups phalanxes.

Ensembling classification models based on phalanxes of variables with applications in drug discovery