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Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory

by Mohammad Masoud Javidi, Nasibeh Emami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 9
Year of Publication: 2018
Authors: Mohammad Masoud Javidi, Nasibeh Emami
10.5120/ijca2018917182

Mohammad Masoud Javidi, Nasibeh Emami . Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory. International Journal of Computer Applications. 181, 9 ( Aug 2018), 1-9. DOI=10.5120/ijca2018917182

@article{ 10.5120/ijca2018917182,
author = { Mohammad Masoud Javidi, Nasibeh Emami },
title = { Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 9 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number9/29797-2018917182/ },
doi = { 10.5120/ijca2018917182 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:27.663084+05:30
%A Mohammad Masoud Javidi
%A Nasibeh Emami
%T Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 9
%P 1-9
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Finding a subset of features from a large data set is a problem that arises in many fields of study. It is important to have an effective subset of features that is selected for the system to provide acceptable performance. This will lead us in a direction that to use meta-heuristic algorithms to find the optimal subset of features. The performance of evolutionary algorithms is dependent on many parameters which have significant impact on its performance, and these algorithms usually use a random process to set parameters. The nature of chaos is apparently random and unpredictable; however it also deterministic, it can suitable alternative instead of random process in meta-heuristic algorithms.

References
  1. Mitra, S. Kundu, P. and Pedrycz, W. 2012. Feature selection using structural similarity. Information Sciences. 198, 48–61.
  2. Kanan, H. R. and Faez, K. 2008. An improved feature selection method based on ant colony optimization (aco) evaluated on face recognition system. Applied Mathematics and Computation. 205, 716–725.
  3. Wang, Y. Dahnoun, N. and Achim, A. 2012. A novel system for robust lane detection and tracking. Signal Processing. 92, 319–334.
  4. Zhou, H. You, M. Liu, L. Zhuang, C. 2017. Sequential data feature selection for human motion recognition via Markov blanket. Pattern Recognition Letters. 86, 18-25.
  5. Mandloi, A. and Gupta, P. 2017. An Effective Modeling for Face Recognition System: LDA and GMM based Approach. International Journal of Computer Applications. 180(1).
  6. Salcedo-Sanz, S. Cornejo-Bueno, L. rieto, L. Paredes, D. and García-Herrera, R. 2018. Feature selection in machine learning prediction systems for renewable energy applications. Review article. Renewable and Sustainable Energy Reviews, 90, 728–741.
  7. Eisa, D. A. Taloba, A. I. and Ismail, S. S. I. 2018. A comparative study on using principle component analysis with different text classifiers. International Journal of Computer Applications. 180(31).
  8. Jain, A. and Zongker, D. 1997. Feature selection: evaluation, application, and small sample performance. Ieee Transactions on Pattern Analysis and Machine Intelligence. 19, 153 - 158.
  9. Novaković, J. Strbac, P. and Bulatović, D. 2011. Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav Journal of Operations Research. 21(1), 119-135.
  10. Chen, Y. Li, Y. Cheng, X. and Guo, L. 2006. Survey and taxonomy of feature selection algorithms in intrusion detection system. Lecture Notes in Computer Science. 4318, 153-167.
  11. Ferreira, A. J. and Figueiredo, M. A. T. 2012. Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters.33, 1794–1804.
  12. Dash, M. and Liu, H. 1997. Feature selection for classification. Intelligent Data Analysis.1, 131–156.
  13. Hall, M. A. 1999. Correlation-based feature selection for machine learning. Doctoral Thesis. University of Waikato.
  14. Aghdam, H. M. Aghaee, G. N. Basiri, M. E. 2009. Text feature selection using ant colony optimization. Expert Systems with Applications. 36, 6843–6853.
  15. Gheyas, I. A. and Smith, L. S. 2010. Feature subset selection in large dimensionality domains. Pattern Recognition. 43, 5-13.
  16. Hua, J. Tembe, W. and Dougherty, E. R. 2008 Feature selection in the classification of high-dimension data. Ieee International Workshop on Genomic Signal Processing and Statistics.1–2.
  17. Jin, X. Xu, A. Bie, R. and Guo, P. 2006. Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles. Lecture Notes in Computer Science. 3916, 106–115.
  18. Liao, C. Li, S. and Luo, Z. 2007. Gene selection using wilcoxon rank sum test and support vector machine for cancer. Lecture Notes in Computer Science. 4456, 57–66.
  19. Peng, H, Long, F. and Ding, C. 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min redundancy. Ieee Transactions on Pattern Analysis and Machine Intelligence. 27, 1226–1238.
  20. Biesiada, J. and Duch, W. 2008. Feature selection for high-dimensional data—a pearson redundancy based filter. Advances in Soft Computing. 45, 242–249.
  21. Rocchi, L. Chiari, and L. Cappello, A. 2004. Feature selection of stabilometric parameters based on principal component analysis. Medical and Biological Engineering and Computing. 42, 71–79.
  22. Kira, K. and Rendell, L. A. 1992. The feature selection problem: traditional methods and a new algorithm, In: Proceedings of Ninth National Conference on Artificial Intelligence. 129–134.
  23. Almuallim, H. and Dietterich, T. G. 1991. Learning with many irrelevant features, In: Proceedings of Ninth National Conference on Artificial Intelligence. 547–552.
  24. Liu, H. and Setiono, R. 1996. A probabilistic approach to feature selection – a filter solution In: Proceedings of Ninth International Conference on Industrial and Engineering Applications of AI and ES. 284–292.
  25. Raman, B. and Ioerger, T. R. 2002. Instance based filter for feature selection. Journal of Machine Learning Research. 1, 1–23.
  26. Jensen, R. and Shen, Q. 2001. A rough set aided system for sorting WWW bookmarks. Web Intelligence: Research and Development. 95–105.
  27. Traina, C. Traina, A. Wu, L. and Faloutsos, C. 2000. Fast feature selection using the fractal dimension, In: Proceedings of the fifteenth Brazilian Symposium on Databases (SBBD). 158–171.
  28. Chen, B. Chen, L. Chen, Y. 2013. Efficient ant colony optimization for image feature selection. Signal Processing 93, 1566–1576.
  29. Peng, H. Long, F. and Ding, C. 2003. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research. 3, 1371–1382.
  30. Guan, S. Liu, J. and Qi, Y. An incremental approach to contribution based feature selection. Journal of Intelligence Systems. 13.
  31. Gasca, E. Sanchez, J. S. and Alonso, R. 2006. Eliminating redundancy and irrelevance using a new MLP-based feature selection method. Pattern Recognition. 39, 313–315.
  32. Hsu, C. Huang, H. and Schuschel, D. 2002 The ANNIGMA-wrapper approach to fast feature selection for neural nets. Ieee Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics. 32, 207–212.
  33. Kabira, M. M. Shahjahan, M. and Murase, K. 2012. A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications. 39, 3747–3763.
  34. Sivagaminathan, R. K. and Ramakrishnan, S. A. 2007. Hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Systems with Applications. 33, 49–60.
  35. Tsai, C. F. Eberle, W. and Chu, C. Y. 2013. Genetic algorithms in feature and instance selection. Knowledge Based Systems. 39, 240-247.
  36. Yang, W. Li, D. and Zhu, L. 2011. An improved genetic algorithm for optimal feature subset selection from multi-character feature set. Expert Systems with Applications. 38, 2733–2740.
  37. Sahu, B. Mishra, D. A. 2012. Novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Engineering. 38, 27-31.
  38. Wang, X. Yang, J. Teng, X. Xia, W. and Jensen, R. 2007. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters. 28, 459–471.
  39. Kennedy, J. and Eberhart, R. C. 1995. Particle swarm optimization. In: Proceedings of the Ieee International Conference on Neural Networks. 4, 1942–1948.
  40. Thangavel, K. Bagyamani, J. Rathipriya, R. 2012. Novel hybrid pso-sa model for biclustering of expression data. Procedia Engineering. 30, 1048 – 1055.
  41. Chuang, L. Yang, C. and Li, J. C. 2011. Chaotic maps based on binary particle swarm optimization for feature selection. Applied Soft Computing. 11, 239–248.
  42. Kennedy, J. and Eberhart, R. C. 1997. A discrete binary version of the particle swarm algorithm. In: Proceedings of the 1997 Conference on Systems, Man, and Cybernetics. 4104–4109.
  43. Unler, A. Murat, A. 2010. A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research. 206, 528–539.
  44. Rostami, N. and Nezamabadi, H. 2006. A new method for binary PSO. In: proceedings of the international Conference on Electronic Engineering (in Persian).
  45. Nickabadi, A. Ebadzadeh, M. M. and Safabakhsh, R. 2011. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing. 11, 3658–3670.
  46. Shen, Yi. Bu, Y. Yuan, M. 2009. A novel chaos particle swarm optimization (pso) and its application in pavement maintance decision. In: Proceedings on Fourth Ieee Conference on Industrial Electronics and Applications. 3521-3526.
  47. Chuang, L. Y. Hsiao, C. J. and Yang, C. H. 2011. Chaotic particle swarm optimization for data clustering, Expert Systems with Applications. 38, 14555–14563.
  48. Jiang, L. Cai, Z. Wang, D. and Jiang, S. 2007. Survey of improving k-nearest-neighbor for classification. In: Proceedings of Fourth International Conference on Fuzzy Systems and Knowledge Discovery . 1, 679 – 683.
  49. Wu, X. et al. 2008. Top 10 algorithms in data mining. Knowl Inf Syst. 14, 1–37.
  50. Tahir, M. A. and Smith, J. Creating diverse nearest-neighbor ensembles using simultaneous metaheuristic feature selection. 2010. Pattern Recognition Letters. 31, 1470–1480.
Index Terms

Computer Science
Information Sciences

Keywords

Feature selection Classification Meta-heuristic algorithm Binary particle swarm optimization Chaos theory.

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