Double mutation and correction to expand the training data space using emerging patterns

The approach of expanding the training data space has been proposed recently in the field of data mining. This approach is aimed at improving the accuracy of different classifiers. The performance of these classifiers depends on the amount of knowledge gained from the training data. The knowledge is proportional to the size of the data space. Different methods have been proposed to expand the data space (hence, the gained knowledge). In this paper, we propose a new data expansion method. We experimentally prove that our method is capable of improving the performance of a classifier more than the previous proposed methods.