International Journal of Mathematical Sciences and Computing(IJMSC)

ISSN: 2310-9025 (Print), ISSN: 2310-9033 (Online)

Published By: MECS Press

IJMSC Vol.2, No.3, Jul. 2016

Effect Neural Networks on Selected Feature by Meta Heuristic Algorithms

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Maysam Toghraee, Farhad rad, Hamid parvin

Index Terms

Feature selection;data mining;algorithm cluster;heuristic methods


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


[1]Dy J. Unsupervised feature selection. Computational Methods of Feature Selection, (2008). pages 19-39.

[2]Guyon I and Elise A. An introduction to variable and feature selection. Journal of Machine Learning Research, (2003).3:1157-1182.

[3]Jain k., Dubes R. C. Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs. (1988).

[4]Lampinen J and Laksone J and Oja E. Pattern recognition. In editor, Image Processing and Pattern Recognition, volume 5 of Neural Network Systems Techniques and Applications, (1998). pages 1- 59. Academic Press.

[5]Ladha L and Scholar R, Depa T, LFeature Selection Methods and Algorithms, International Journal on Computer Science and Engineering (IJCSE), Vol. 3, No. 5, (2011). pp. 1787–1797.

[6]Marki F and Vogel M and Fischer M. "Process Plan optimization using a Genetic Algorithm", PATAT, (2006), pp. 528–531. ISBN 80-210-3726-1.

[7]Marki F and Vogel M and Fischer M. "Process Plan optimization using a Genetic Algorithm", PATAT, (2006). pp. 528–531. ISBN 80-210-3726-1.

[8]Masoudian S and ESTEKI A., "Design schedule automatically using genetic algorithm ", thesis, university of Isfahan. (1386).

[9]Matlab version, 29 january 2012, U.S.Patents Carol Meyers and James B. Orlin, (2011), "Very Large-Scale Neighborhood Search Techniques in Timetabling Problems", PATAT 2011, pp. 36–52. ISBN 80-210-3726-1.

[10]Mehdi A." Introduction to genetic algorithm and application", Tehran: Bell Press naghos. (1386).

[11]Murata S and Kurova H. Self-Organization of Biological Systems. (2012).

[12]Neumann J, C and Schnar G. S. Combined SVM-based feature selection and classification, Machine Learning, (2005). Vol. 61, No. 3, pp. 129 – 150.

[13]Perzina R. "Solving the University Timetabling Problem with Optimized Enrolment of Students by aParallel Self-adaptive Genetic Algorithm", (2006). PATAT 2006, pp. 264–280. ISBN 80-210-3726-1.

[14]Susana M and Vieira J and Sousa M.C. Fuzzy criteria for feature selection, Fuzzy Sets and Systems, (2012).Vol. 189, No. 1, pp. 1–18.

[15]Zhao Z and Liu H. Semi-supervised feature selection via spectral analysis. In Proceedings of SIAM International Conference on Data Mining (SDM). (2007).