Effect Neural Networks on Selected Feature by Meta Heuristic Algorithms

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Author(s)

Maysam Toghraee 1,* Farhad rad 2 Hamid parvin 1,2

1. Faculty of engineering, department of computer science, Islamic Azad University, Kohgilouye-vaBoyerahmad, Isfahan and 8661854381, Iran

2. Faculty of engineering, department of computer science, Islamic Azad University, Kohgilouye-vaBoyerahmad, Yasouj, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2016.03.04

Received: 1 Apr. 2016 / Revised: 5 May 2016 / Accepted: 2 Jun. 2016 / Published: 8 Jul. 2016

Index Terms

Feature selection, data mining, algorithm cluster, heuristic methods

Abstract

Feature selection is one of the issues that have been raised in the discussion of machine learning and statistical identification model. We have provided definitions for feature selection and definitions needed to understand this issue, we check. Then, different methods for this problem were based on the type of product, as well as how to evaluate candidate subsets of features, we classify the following categories. As in previous studies may not have understood that different methods of assessment data into consideration, We propose a new approach for assessing similarity of data to understand the relationship between diversity and stability of the data is selected. After review and meta-heuristic algorithms to implement the algorithm found that the cluster algorithm has better performance compared with other algorithms for feature selection sustained.

Cite This Paper

Maysam Toghraee, Farhad rad, Hamid parvin,"Effect Neural Networks on Selected Feature by Meta Heuristic Algorithms", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.2, No.3, pp.41-48, 2016.DOI: 10.5815/ijmsc.2016.03.04

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