Classification algorithms
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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Data mining: practical machine learning tools and techniques with Java implementations
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Simplifying decision trees: A survey
The Knowledge Engineering Review
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Reverse Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Reverse k-nearest neighbor search in dynamic and general metric databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
10-year CVD risk prediction and minimization via InverseClassification
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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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.