In this paper,we examine an emerging variation of the classification problem,which is known as the inverse classification problem.In this problem,we determine the features to be used to create a record which will result in a desired class label.Such an approach is useful in applications in which it is an objective to determine a set of actions to be taken in order to guide the data mining application towards a desired solution.This system can be used for a variety of decision support applications which have pre-determined task criteria.We will show that the inverse classification problem is a powerful and general model which encompasses a number of different criteria.We propose a number of algorithms for the inverse classification problem,which use an inverted list representation for intermediate data structure representation and classification.We validate our approach over a number of real datasets.