Classification of Closed Frequent Patterns Improved by Feature Space Transformation

In some real-world applications, the predefined features are not discriminative enough to represent well the distinctiveness of different classes. Therefore, building a more well-defined feature space becomes an urgent task. The main goal of feature space transformation is to map a set of features defined in a space into a new more powerful feature space so that the classification based on the transformed data can achieve performance gain compared to the performance in the original space. In this paper, we introduce a feature transformation method in which the feature transformation is conducted using the closed frequent patterns. Experiments on real-world datasets show that the transformed features obtained from combining the closed frequent patterns and the original features are superior in terms of classification accuracy than the approach based solely on closed frequent patterns.