In this paper, we discuss the inverse classification problem, in which 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 data sets.
By: Charu C. Aggarwal; Chen Chen; Jiawei Han
Published in: RC24776 in 2009
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