1. A method for classifying structural input data, the method comprising a computer system performing steps of: constructing multiple classifiers, wherein each classifier is constructed on a subset of training data, using selected composite features from the subset of training data, the composite features being selected by iteratively applying a feature selection step wherein multiple disjoint feature sets are identified to represent the structural input data in different feature spaces, and the structural input data, when characterized by a skewed prior class distribution, being subjected to a sampling step to obtain a balanced class distribution; and computing a consensus among the multiple classifiers in accordance with a voting scheme such that at least a portion of the structural input data is assigned to a particular class in accordance with the computed consensus; wherein the computer system comprises a memory and a processor device operatively coupled to the memory.