Function decomposition is a machine learning algorithm that induces the target concept in the form of a hierarchy of intermediate concepts and their definitions. Though it is effective in discovering the concept structure hidden in the training data, it suffers much from under sampling. In this paper, we propose an oracle based meta learning method that generates new examples with the help of a bagged ensemble to induce accurate classifiers when the training data sets are small. Here the values of new examples to be generated and the number of such examples required are automatically determined by the algorithm. Previous work in this area deals with the generation of fixed number of random examples irrespective of the size of the training set's attribute space. Experimental analysis on different sized data sets shows that our algorithm significantly improves accuracy of function decomposition and is superior to existing meta-learning method. © Springer-Verlag Berlin Heidelberg 2004.