Comparison of Instance Selection and Construction Methods with Various Classifiers

Abstract

Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Other methods are less efficient or provide low compression ratio.