Dataset Editing Techniques: A Comparative Study

Editing techniques remove examples from datasets with the goal to obtain more accurate and faster classifiers. The objective of this paper is to compare several popular dataset editing techniques with respect to classification accuracy and training set compression rate including Wilson editing, Citation editing, and Multi-edit. Moreover, supervised clustering editing is introduced which replaces examples belonging to a cluster by a cluster representative. Furthermore, we explore the benefits of replacing datasets by support vectors that are commonly used in Support Vector Machine (SVM). We also discuss the results of experiments that compare and analyze the relationships between the editing techniques investigated by using a benchmark consisting of UCI and artificial 2D spatial datasets. Our empirical evaluation shows that editing techniques, in general, improve the classification accuracy of a 1-NN classifier significantly, leading to more efficient and accurate classifiers for most of the datasets tested. The experimental results show a strong performance for Wilson, Citation, and supervised clustering editing and poor performance by Multi-edit and SVM editing with respect to classification accuracy. Furthermore, training set compression rates reported by supervised clustering editing were superior to all other editing techniques investigated.