Notice of RetractionAlgorithm for classification based on positive and negative class association rules

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

The negative class association rules are important to build accurate and efficient classifiers. Despite a great deal of research, a number of challenges still exist. In order to solve the problem of “difficult to build precise classifier”, the paper presents a new algorithm for classification which integrates positive class association rules and negative class association rules. The algorithm applies Apriori method and correlation between itemsets and class labels to compute all positive and negative class association rules from training dataset. Moreover, a classifier will be built to predict the label of a new data object. The performance study shows that the method is highly efficient and accurate in comparison with other reported associative classification methods.