Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal C, Han J, Wang J, Yu P. A framework for on-demand classification of evolving data streams. May 2006, 18(5): 577–589.
Alsabti K, Ranka S, Singh V. CLOUDS: A decision tree classifier for large datasets. In Proc. KDD, New York, USA, Aug. 27–31, 1998, pp.2–8.
Breiman L, Friedman J, Olshen R A, Stone C J. Classification and Regression Trees. Chapman & Hall, 1984.
Brodley C E, Utgoff P E. Multivariate decision trees. Machine Learning, 1995, 19(1): 45–77.
Breslow L, Aha D. Simplifying decision trees. Knowledge Engineering Review, 1997, 12(1): 1–40.
Duda R, Hart P, Stork D. Pattern Classification. 2nd Edition, New York: John Wiley and Sons Inc., 2001.
Friedman J H. A recursive partitioning decision rule for non-parametric classifiers. IEEE Transactions on Computers, 1977, 26(4): 404–408.
Gehrke J, Ganti V, Ramakrishnan R, Loh W Y. BOAT: Optimistic decision tree construction. In Proc. ACM SIGMOD Int. Conf. Management of Data, Philadelphia, USA, May 31–June 3, 1999, pp.169–180.
James M. Classification Algorithms. Wiley, 1985.
Quinlan J R. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
Smyth P, Gray A, Fayyad U M. Retrofitting decision tree classifiers using kernel density estimation. In Proc. the International Conference on Machine Learning, Taheo City, USA, July 9–12, 1995, pp.506–514.
Achtert E, Kriegel H P, Kröger P, Renz M, Züfle A. Reverse k-nearest neighbor search in dynamic and general metric databases. In Proc. EDBT, Saint Petersburg, Russia, Mar. 24–26, 2009, pp.886–897.
Tao Y, Yiu M L, Mamoulis N. Reverse nearest neighbor search in metric spaces. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(9): 1239–1252.
Kaelbling L, Littman M, Moore A. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 1996, 4: 237–285.
Sutton R, Barto A. Re-Inforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1988.
Hettich S, Blake C, Merz C. UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, 1998, http://archive.ics.uci.edu/ml.
Witten I, Frank E. Data Mining: Practical Machine Learning Tools with Java Implementations. San Francisco: Morgan Kaufmann Publishers, CA, 2000, http://www.cs.waikato.ac.nz/∼ml/weka/book.html.
Kohavi R. The power of decision tables. In Proc. European Conference on Machine Learning, Crete, Greece, Apr. 25–27, 1995, pp.174–189.
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is an extended version of a paper published in IEEE ICDE Conference, 2006.
Rights and permissions
About this article
Cite this article
Aggarwal, C.C., Chen, C. & Han, J. The Inverse Classification Problem. J. Comput. Sci. Technol. 25, 458–468 (2010). https://doi.org/10.1007/s11390-010-9337-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-010-9337-x