## 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.

Original language | English (US) |
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Pages (from-to) | 458-468 |

Number of pages | 11 |

Journal | Journal of Computer Science and Technology |

Volume | 25 |

Issue number | 3 |

DOIs | |

State | Published - May 2010 |

Externally published | Yes |

## Keywords

- Classification
- Data mining
- Inverse optimization
- Prediction
- Re-inforcement learning

## ASJC Scopus subject areas

- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Science Applications
- Computational Theory and Mathematics