The Inverse Classiflcation Problem
Charu C. Aggarwal, Chen Chen, Jiawei Han · 2010
In this paper, we examine an emerging variation of the classiflcation problem, which is known as the inverse classiflcation 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 classiflcation problem is a powerful and general model which encompasses a number of difierent criteria. We propose a number of algorithms for the inverse classiflcation problem, which use an inverted list representation for intermediate data structure representation and classiflcation. We validate our approach over a number of real datasets.