A Multi-Verse Optimizer Approach for Instance Selection and Optimizing 1-NN Algorithm

A Multi-Verse Optimizer Approach for Instance Selection and Optimizing 1-NN Algorithm

Nassima Dif, Zakaria Elberrichi
DOI: 10.4018/IJSITA.2018040103
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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.
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2. State Of Art

Instance and feature selection have been widely studied in numerous works in the literature to solve the problem of superfluous instances and irrelevant and noisy features by using both filter and wrapper approaches.

2.1. Filter Methods

There are numerous filter methods proposed in literature because of their reduced execution time compared to the wrapper methods. The Table 1 resume some filter methods for instance selection detailed in (Olvera-Lopez et al., 2010).

Table 1.
Filter methods.
(Paredes, & Vidal,2000)WP (weighting prototypes)
(Riquelme, Aguilar-Ruiz, & Toro,2003)POP (pattern by ordered projections)
(Raicharoen, & Lursinsap,2005).POC-NN (pair opposite class-nearest neighbor)
(Narayan, Murthy, & Pal,2006)Maxdi_kd-trees
(Lumini, & Nanni, 2006)CLU (clustering)
(Olvera-Lopez et al.,2007)OSC (object selection)
(Olvera-Lopez et al.,2008)PSR (prototype selection by relevance)

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