Instance selection and feature selection are important steps in the data mining process. They help reduce the excessive number of instances and features. The purpose of this reduction is to eliminate the noisy and redundant instances and features in order to improve the classifiers performance. Various related works in the literature proves that metaheuristics can resolve the problem of instance and feature selection. In this article, the authors propose a new instance selection approach based on a Multi- Verse Optimizer algorithm (MVOIS), to reduce the run time and improve the performance of the one nearest neighbor classifier (1NN). This article tested the proposed approach on 31 datasets from the UCI repository and performed three more pre-process ISFS, FS and FSIS. The comparative study illustrates the efficiency of ISFS and FSIS compared to FS and IS. ISFS achieved 100% accuracy for labor and iris datasets.
A pattern synthesis technique with an efficient nearest neighbor classifier for binary pattern recognition
