Search WWW Search inass.org
»Journal Description
»Topics
»Call for Papers and Reviewers
»Author Guidelines
»Contents & Papers
»Call for Special Issues
»SCOPUS
 
»IEEE CIS
»INNS
»IEEE IS
DOI: http://dx.doi.org/10.22266/ijies2017.0430.03

Feature Selection Optimization using Hybrid Relief-f with Self-adaptive Differential Evolution

Author(s):

M.N.Shah Zainudin1,2*, Md Nasir Sulaiman1, Norwati Mustapha1, Thinagaran Perumal1, Azree Shahrel Ahmad Nazri1, Raihani Mohamed1, Syaifulnizam Abd Manaf1


Affiliations:

1Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia







Abstract:

In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository.


Keywords:

Relief-f, Differential evolution, Evolutionary algorithm, Optimization.


Full Text:




References:
  1. R. A. Rahim, N. Othman, M. N. S. Zainudin, N. A. Ali, and M. M. Ismail, “Iris Recognition using Histogram Analysis via LPQ and RI-LPQ Method,” no. 4, pp. 66–70, 2012.
  2. G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, 2014.
  3. J. Tang, S. Alelyani, and H. Liu, “Feature Selection for Classification : A Review,” Data Classif. Algorithms Appl., pp. 37–64, 2014.
  4. M. N. S. Zainudin, M. Mohd Said, and M. . Ismail, “Feature Extraction on Medical Image Using 2D Gabor Filter,” Appl. Mech. Mater., vol. 52–54, pp. 2128–2132, 2011.
  5. M. N. S. Zainudin, H. . Radi, and S. . Abdullah, “Face Recognition using Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA),” Ijens.Org, no. 5, 2012.
  6. J. Apolloni, G. Leguizamón, and E. Alba, “Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments,” Appl. Soft Comput. J., vol. 38, pp. 922–932, 2016.
  7. M. N. S. Zainudin, N. Sulaiman, N. Mustapha, and T. Perumal, “Activity Recognition based on Accelerometer Sensor using Combinational Classifiers,” pp. 68–73, 2015.
  8. V. Bolon-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, “A review of feature selection methods on synthetic data,” Knowl. Inf. Syst., vol. 34, no. 3, pp. 483–519, 2013.
  9. J. A. Olvera-Lopez, J. A. Carrasco-Ochoa, J. F. Martinez-Trinidad, and J. Kittler, “A review of instance selection methods,” Artif. Intell. Rev., vol. 34, no. 2, pp. 133–143, 2010.
  10. R. N. Khushaba, A. Al-Ani, A. AlSukker, and A. Al-Jumaily, “A Combined Ant Colony and Differential Evolution Feature Selection Algorithm,” Ant Colony Optim. Swarm Intell., vol. 5217, pp. 1–12, 2008.
  11. M. Robnik-Siknja and I. Kononeko, “Theoretical and empirical analysis of RelifF and RReliefF,” Mach Learn, vol. 53, pp. 23–69, 2003.
  12. R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim., pp. 341–359, 1997.
  13. R. N. Khushaba, A. Al-Ani, and A. Al-Jumaily, “Feature subset selection using differential evolution and a statistical repair mechanism,” Expert Syst. Appl., vol. 38, no. 9, pp. 11515–11526, 2011.
  14. Q. Zou, J. Zeng, L. Cao, and R. Ji, “A novel features ranking metric with application to scalable visual and bioinformatics data classification,” Neurocomputing, vol. 173, pp. 346–354, 2016.
  15. V. Santos, N. Datia, and M. P. M. Pato, “Ensemble Feature Ranking Applied to Medical Data,” Procedia Technol., vol. 17, pp. 223–230, 2014.
  16. N. Challita, M. Khalil, and P. Beauseroy, “New technique for feature selection: combination between Elastic Net and Relief,” 2015, pp. 3–8.
  17. N. A. Capela, E. D. Lemaire, and N. Baddour, “Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients,” PLoS One, vol. 10, no. 4, pp. 1–18, 2015.
  18. V. Bolon-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, “Data classification using an ensemble of filters,” Neurocomputing, vol. 135, pp. 13–20, 2014.
  19. A. Ghosh, A. Datta, and S. Ghosh, “Self-adaptive differential evolution for feature selection in hyperspectral image data,” Appl. Soft Comput. J., vol. 13, no. 4, pp. 1969–1977, 2013.
  20. A. Al-Ani, A. Alsukker, and R. N. Khushaba, “Feature subset selection using differential evolution and a wheel based search strategy,” Swarm Evol. Comput., vol. 9, pp. 15–26, 2013.
  21. U. K. Sikdar, A. Ekbal, and S. Saha, “Differential Evolution based Feature Selection and Classifier Ensemble for Named Entity Recognition,” Proc. COLING 2012, vol. 11, no. i, p. 11218, 2012.
  22. P. T. Bharathi and P. Subashini, “Optimal Feature Subset Selection Using Differential Evolution and Extreme Learning Machine,” Int. J. Sci. Res., vol. 3, no. 7, pp. 1898–1905, 2014.
  23. B. Fish and a Khan, “Feature selection based on mutual information for human activity recognition,” … , Speech Signal …, pp. 1729–1732, 2012.
  24. A. Idris, M. Rizwan, and A. Khan, “Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies,” Comput. Electr. Eng., vol. 38, no. 6, pp. 1808–1819, 2012.
  25. I. Kononenko, “Estimating attributes: Analysis and extensions of RELIEF,” Mach. Learn. ECML-94, vol. 784, pp. 171–182, 1994.
  26. R. Akhavian and A. H. Behzadan, “Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers,” Adv. Eng. Informatics, vol. 29, no. 4, pp. 867–877, 2015.
  27. A. K. Palit and D. Popovic, Computational Intelligence in Time Series Forecasting. 2005.
  28. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  29. M. N. S. Zainudin, M. N. Sulaiman, N. Mustapha, T. Perumal, and A. S. Ahmad Nazri, “Hybrid Relief-f Differential Evolution Feature Selection for Accelerometer Actions,” Adv. Sci. Lett., vol. 4, no. 2, pp. 400–407, 2016.
  30. A. M.a, K. A.a, and A. S.I.b, “Classification of Physical Activities Using Wearable Sensors,” Intell. Autom. Soft Comput., no. September 2016, pp. 1–10, 2015.
  31. S. Chernbumroong, S. Cang, and H. Yu, “Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People,” vol. 19, no. 1, pp. 282–289, 2015.

INASS Home | Copyright@2008 The Intelligent Networks and Systems Society