International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 8, No. 10, 8 Oct. 2016
Cover page and Table of Contents: PDF (size: 819KB)
The Impact of Feature Selection on Meta-Heuristic Algorithms to Data Mining Methods
Full Text (PDF, 819KB), PP.33-39
Views: 0 Downloads: 0
Author(s)
Maysam Toghraee
1,*
Hamid parvin
2
Farhad rad
2
Index Terms
Feature selection, data mining, algorithm cluster, heuristic methods, meta-heuristic
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 has better performance compared with other algorithms for feature selection sustained.
Cite This Paper
Maysam Toghraee, Hamid Parvin, Farhad Rad, "The Impact of Feature Selection on Meta-Heuristic Algorithms to Data Mining Methods", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.10, pp.33-39, 2016. DOI:10.5815/ijmecs.2016.10.05
Reference
[1]Camp C.V. “Design of space trusses using Big Bang – Big Crunch optimization”, Journal of Structural Engineering, vol.133, Issue 7, (2007 ). pp.999-1008.
[2]Dy J. Unsupervised feature selection. Computational Methods of Feature Selection, (2008). pages 19-39.
[3]Erol Osman k and Eksin I. “New optimization method : Big Bang-Big Crunch”, Elsevier, Advances in Engineering Software .(2006).37 pp: 106–111.
[4]Guyon I and Elise A. An introduction to variable and feature selection. Journal of Machine Learning Research, ( 2003).3:1157-1182.
[5]Jain k ,. Dubes R. C. Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs. (2010).
[6]Karaboga D, Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. (2011).
[7]Kennedy J and Eberhart R, C. “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, .(2008). pp: 1942- 948.
[8]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.
[9]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.
[10]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.
[11]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.
[12]Masoudian S and ESTEKI A,." Design schedule automatically using genetic algorithm ", thesis, university of Isfahan. .(2007).
[13]Matlab version 7.4.0.287(R2012a), 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.
[14]Mehdi A." Introduction to genetic algorithm and application", Tehran: Bell Press naghos. (2009).
[15]Murata S and Kurova H. Self-Organization of Biological Systems. (2013).
[16]Neumann J, C and Schnar G. S. Combined SVM-based feature selection and classification, Machine Learning, (2005). Vol. 61, No. 3, pp. 129 – 150
[17]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.
[18]Pham D. T and Ghanbarzadeh A and Koc E nad Otri S and Rahim S., Zaidi M. The Bees Algorithm –A Novel Tool for Complex Optimization Problems. (2006).
[19]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.
[20]Zhao Z and Liu H. Semi-supervised feature selection via spectral analysis. In Proceedings of SIAM International Conference on Data Mining (SDM). (2007).